diff --git "a/unet/coreml_model.mlmodelc/model.mil" "b/unet/coreml_model.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/unet/coreml_model.mlmodelc/model.mil" @@ -0,0 +1,10224 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor text_embeds, tensor time_ids, tensor timestep) { + tensor var_24 = const()[name = tensor("op_24"), val = tensor(-1)]; + tensor var_41_axes_0 = const()[name = tensor("op_41_axes_0"), val = tensor([1])]; + tensor var_41_cast_fp16 = expand_dims(axes = var_41_axes_0, x = timestep)[name = tensor("op_41_cast_fp16")]; + tensor var_43_to_fp16 = const()[name = tensor("op_43_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast_fp16 = mul(x = var_41_cast_fp16, y = var_43_to_fp16)[name = tensor("emb_3_cast_fp16")]; + tensor var_48_cast_fp16 = sin(x = emb_3_cast_fp16)[name = tensor("op_48_cast_fp16")]; + tensor var_49_cast_fp16 = cos(x = emb_3_cast_fp16)[name = tensor("op_49_cast_fp16")]; + tensor emb_7_interleave_0 = const()[name = tensor("emb_7_interleave_0"), val = tensor(false)]; + tensor emb_7_cast_fp16 = concat(axis = var_24, interleave = emb_7_interleave_0, values = (var_48_cast_fp16, var_49_cast_fp16))[name = tensor("emb_7_cast_fp16")]; + tensor var_53_begin_0 = const()[name = tensor("op_53_begin_0"), val = tensor([0, 160])]; + tensor var_53_end_0 = const()[name = tensor("op_53_end_0"), val = tensor([1, 320])]; + tensor var_53_end_mask_0 = const()[name = tensor("op_53_end_mask_0"), val = tensor([true, true])]; + tensor var_53_cast_fp16 = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = emb_7_cast_fp16)[name = tensor("op_53_cast_fp16")]; + tensor var_55_begin_0 = const()[name = tensor("op_55_begin_0"), val = tensor([0, 0])]; + tensor var_55_end_0 = const()[name = tensor("op_55_end_0"), val = tensor([1, 160])]; + tensor var_55_end_mask_0 = const()[name = tensor("op_55_end_mask_0"), val = tensor([true, false])]; + tensor var_55_cast_fp16 = slice_by_index(begin = var_55_begin_0, end = var_55_end_0, end_mask = var_55_end_mask_0, x = emb_7_cast_fp16)[name = tensor("op_55_cast_fp16")]; + tensor sample_3_interleave_0 = const()[name = tensor("sample_3_interleave_0"), val = tensor(false)]; + tensor sample_3_cast_fp16 = concat(axis = var_24, interleave = sample_3_interleave_0, values = (var_53_cast_fp16, var_55_cast_fp16))[name = tensor("sample_3_cast_fp16")]; + tensor var_58 = const()[name = tensor("op_58"), val = tensor(1)]; + tensor var_65_axes_0 = const()[name = tensor("op_65_axes_0"), val = tensor([-1])]; + tensor var_65_cast_fp16 = expand_dims(axes = var_65_axes_0, x = sample_3_cast_fp16)[name = tensor("op_65_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = var_65_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_69 = const()[name = tensor("op_69"), val = tensor([1, 1])]; + tensor var_71 = const()[name = tensor("op_71"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307712))), name = tensor("time_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor([1280, 320, 1, 1])]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307904)))]; + tensor input_3_cast_fp16 = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_71, groups = var_58, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_69, weight = time_embedding_linear_1_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor emb_pad_type_0 = const()[name = tensor("emb_pad_type_0"), val = tensor("custom")]; + tensor emb_pad_0 = const()[name = tensor("emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539392))), name = tensor("time_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539584)))]; + tensor emb_cast_fp16 = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_79, groups = var_58, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_77, weight = time_embedding_linear_2_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = tensor("emb_cast_fp16")]; + tensor concat_0 = const()[name = tensor("concat_0"), val = tensor([6])]; + tensor timesteps_cast_fp16 = reshape(shape = concat_0, x = time_ids)[name = tensor("timesteps_cast_fp16")]; + tensor var_85 = const()[name = tensor("op_85"), val = tensor(-1)]; + tensor var_102_axes_0 = const()[name = tensor("op_102_axes_0"), val = tensor([1])]; + tensor var_102_cast_fp16 = expand_dims(axes = var_102_axes_0, x = timesteps_cast_fp16)[name = tensor("op_102_cast_fp16")]; + tensor var_104_to_fp16 = const()[name = tensor("op_104_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1542208)))]; + tensor emb_11_cast_fp16 = mul(x = var_102_cast_fp16, y = var_104_to_fp16)[name = tensor("emb_11_cast_fp16")]; + tensor var_109_cast_fp16 = sin(x = emb_11_cast_fp16)[name = tensor("op_109_cast_fp16")]; + tensor var_110_cast_fp16 = cos(x = emb_11_cast_fp16)[name = tensor("op_110_cast_fp16")]; + tensor emb_15_interleave_0 = const()[name = tensor("emb_15_interleave_0"), val = tensor(false)]; + tensor emb_15_cast_fp16 = concat(axis = var_85, interleave = emb_15_interleave_0, values = (var_109_cast_fp16, var_110_cast_fp16))[name = tensor("emb_15_cast_fp16")]; + tensor var_114_begin_0 = const()[name = tensor("op_114_begin_0"), val = tensor([0, 128])]; + tensor var_114_end_0 = const()[name = tensor("op_114_end_0"), val = tensor([6, 256])]; + tensor var_114_end_mask_0 = const()[name = tensor("op_114_end_mask_0"), val = tensor([true, true])]; + tensor var_114_cast_fp16 = slice_by_index(begin = var_114_begin_0, end = var_114_end_0, end_mask = var_114_end_mask_0, x = emb_15_cast_fp16)[name = tensor("op_114_cast_fp16")]; + tensor var_116_begin_0 = const()[name = tensor("op_116_begin_0"), val = tensor([0, 0])]; + tensor var_116_end_0 = const()[name = tensor("op_116_end_0"), val = tensor([6, 128])]; + tensor var_116_end_mask_0 = const()[name = tensor("op_116_end_mask_0"), val = tensor([true, false])]; + tensor var_116_cast_fp16 = slice_by_index(begin = var_116_begin_0, end = var_116_end_0, end_mask = var_116_end_mask_0, x = emb_15_cast_fp16)[name = tensor("op_116_cast_fp16")]; + tensor time_embeds_1_interleave_0 = const()[name = tensor("time_embeds_1_interleave_0"), val = tensor(false)]; + tensor time_embeds_1_cast_fp16 = concat(axis = var_85, interleave = time_embeds_1_interleave_0, values = (var_114_cast_fp16, var_116_cast_fp16))[name = tensor("time_embeds_1_cast_fp16")]; + tensor var_124 = const()[name = tensor("op_124"), val = tensor([1, -1])]; + tensor time_embeds_cast_fp16 = reshape(shape = var_124, x = time_embeds_1_cast_fp16)[name = tensor("time_embeds_cast_fp16")]; + tensor var_127 = const()[name = tensor("op_127"), val = tensor(-1)]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast_fp16 = concat(axis = var_127, interleave = sample_interleave_0, values = (text_embeds, time_embeds_cast_fp16))[name = tensor("sample_cast_fp16")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor(1)]; + tensor var_136_axes_0 = const()[name = tensor("op_136_axes_0"), val = tensor([-1])]; + tensor var_136_cast_fp16 = expand_dims(axes = var_136_axes_0, x = sample_cast_fp16)[name = tensor("op_136_cast_fp16")]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7_cast_fp16 = expand_dims(axes = input_7_axes_0, x = var_136_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor var_140 = const()[name = tensor("op_140"), val = tensor([1, 1])]; + tensor var_142 = const()[name = tensor("op_142"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1542528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4245952))), name = tensor("add_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor([1280, 2816, 1, 1])]; + tensor add_embedding_linear_1_bias_to_fp16 = const()[name = tensor("add_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4246144)))]; + tensor input_9_cast_fp16 = conv(bias = add_embedding_linear_1_bias_to_fp16, dilations = var_142, groups = var_129, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_140, weight = add_embedding_linear_1_weight_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_148 = const()[name = tensor("op_148"), val = tensor([1, 1])]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 1])]; + tensor aug_emb_pad_type_0 = const()[name = tensor("aug_emb_pad_type_0"), val = tensor("custom")]; + tensor aug_emb_pad_0 = const()[name = tensor("aug_emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4248768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5477632))), name = tensor("add_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor add_embedding_linear_2_bias_to_fp16 = const()[name = tensor("add_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5477824)))]; + tensor aug_emb_cast_fp16 = conv(bias = add_embedding_linear_2_bias_to_fp16, dilations = var_150, groups = var_129, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_148, weight = add_embedding_linear_2_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("aug_emb_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = emb_cast_fp16, y = aug_emb_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor(1)]; + tensor var_161 = const()[name = tensor("op_161"), val = tensor([1, 1])]; + tensor var_163 = const()[name = tensor("op_163"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5480448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5489152))), name = tensor("conv_in_weight_to_fp16_palettized"), shape = tensor([320, 4, 3, 3])]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5489344)))]; + tensor input_13_cast_fp16 = conv(bias = conv_in_bias_to_fp16, dilations = var_163, groups = var_158, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_161, weight = conv_in_weight_to_fp16_palettized, x = sample)[name = tensor("input_13_cast_fp16")]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_13_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5490048)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5490752)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5491456)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5492160)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor input_17_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor var_190 = const()[name = tensor("op_190"), val = tensor([1, 1])]; + tensor var_192 = const()[name = tensor("op_192"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5492864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6184128))), name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6184320)))]; + tensor hidden_states_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_192, groups = var_172, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_190, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor input_21_cast_fp16 = silu(x = input_19_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_198 = const()[name = tensor("op_198"), val = tensor([1, 1])]; + tensor var_200 = const()[name = tensor("op_200"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6185024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6492288))), name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6492480)))]; + tensor temb_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_200, groups = var_172, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_198, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_1_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = hidden_states_1_cast_fp16, y = temb_1_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_23_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6493184)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6493888)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_27_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor var_210 = const()[name = tensor("op_210"), val = tensor([1, 1])]; + tensor var_212 = const()[name = tensor("op_212"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6494592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7185856))), name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7186048)))]; + tensor hidden_states_3_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_212, groups = var_172, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_210, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = tensor("hidden_states_3_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = input_13_cast_fp16, y = hidden_states_3_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = input_29_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7186752)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7187456)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = tensor("add_5_cast_fp16")]; + tensor input_33_cast_fp16 = silu(x = add_5_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7188160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7879424))), name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7879616)))]; + tensor hidden_states_5_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_229, groups = var_172, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_227, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor([1, 1])]; + tensor var_237 = const()[name = tensor("op_237"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7880320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8187584))), name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8187776)))]; + tensor temb_3_cast_fp16 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_237, groups = var_172, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_235, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_3_cast_fp16")]; + tensor input_37_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = temb_3_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_37_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8188480)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8189184)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_41_cast_fp16")]; + tensor var_247 = const()[name = tensor("op_247"), val = tensor([1, 1])]; + tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8189888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8881152))), name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8881344)))]; + tensor hidden_states_7_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_249, groups = var_172, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_247, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_29_cast_fp16, y = hidden_states_7_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor var_256 = const()[name = tensor("op_256"), val = tensor([2, 2])]; + tensor var_258 = const()[name = tensor("op_258"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8882048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9573312))), name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9573504)))]; + tensor input_45_cast_fp16 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_258, groups = var_172, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_256, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor var_266 = const()[name = tensor("op_266"), val = tensor(3)]; + tensor var_282 = const()[name = tensor("op_282"), val = tensor(1)]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 10, 64, 64])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_45_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 320, 64, 64])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9574208)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9574912)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_49_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor var_305 = const()[name = tensor("op_305"), val = tensor([1, 1])]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9575616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10958080))), name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 320, 3, 3])]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10958272)))]; + tensor hidden_states_9_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_307, groups = var_282, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_305, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_49_cast_fp16)[name = tensor("hidden_states_9_cast_fp16")]; + tensor var_313 = const()[name = tensor("op_313"), val = tensor([1, 1])]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10959616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11574080))), name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11574272)))]; + tensor temb_5_cast_fp16 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_315, groups = var_282, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_313, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_5_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = temb_5_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = input_53_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11575616)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11576960)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11578304)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11579648)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor input_57_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor var_325 = const()[name = tensor("op_325"), val = tensor([1, 1])]; + tensor var_327 = const()[name = tensor("op_327"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11580992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14345856))), name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14346048)))]; + tensor hidden_states_11_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_327, groups = var_282, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_325, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_332 = const()[name = tensor("op_332"), val = tensor([1, 1])]; + tensor var_334 = const()[name = tensor("op_334"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14347392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14501056))), name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 320, 1, 1])]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14501248)))]; + tensor x_1_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_334, groups = var_282, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_332, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_45_cast_fp16)[name = tensor("x_1_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = add(x = x_1_cast_fp16, y = hidden_states_11_cast_fp16)[name = tensor("hidden_states_13_cast_fp16")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = hidden_states_13_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14502592)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14503936)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor var_356 = const()[name = tensor("op_356"), val = tensor([1, 1])]; + tensor var_358 = const()[name = tensor("op_358"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_type_0 = const()[name = tensor("hidden_states_15_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14505280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14812544))), name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14812736)))]; + tensor hidden_states_15_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_358, groups = var_282, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_356, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_13_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor var_363 = const()[name = tensor("op_363"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_1_cast_fp16 = reshape(shape = var_363, x = hidden_states_15_cast_fp16)[name = tensor("inputs_1_cast_fp16")]; + tensor hidden_states_17_axes_0 = const()[name = tensor("hidden_states_17_axes_0"), val = tensor([1])]; + tensor hidden_states_17_gamma_0_to_fp16 = const()[name = tensor("hidden_states_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14814080)))]; + tensor hidden_states_17_beta_0_to_fp16 = const()[name = tensor("hidden_states_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14815424)))]; + tensor var_379_to_fp16 = const()[name = tensor("op_379_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_17_cast_fp16 = layer_norm(axes = hidden_states_17_axes_0, beta = hidden_states_17_beta_0_to_fp16, epsilon = var_379_to_fp16, gamma = hidden_states_17_gamma_0_to_fp16, x = inputs_1_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor var_396 = const()[name = tensor("op_396"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14816768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15124032))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_1_cast_fp16 = conv(dilations = var_396, groups = var_282, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_394, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_17_cast_fp16)[name = tensor("q_1_cast_fp16")]; + tensor var_400 = const()[name = tensor("op_400"), val = tensor([1, 1])]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15124224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15431488))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_1_cast_fp16 = conv(dilations = var_402, groups = var_282, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_400, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_17_cast_fp16)[name = tensor("k_1_cast_fp16")]; + tensor var_406 = const()[name = tensor("op_406"), val = tensor([1, 1])]; + tensor var_408 = const()[name = tensor("op_408"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15431680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15738944))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_1_cast_fp16 = conv(dilations = var_408, groups = var_282, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_406, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_17_cast_fp16)[name = tensor("v_1_cast_fp16")]; + tensor var_412 = const()[name = tensor("op_412"), val = tensor([1, 10, 64, -1])]; + tensor var_413_cast_fp16 = reshape(shape = var_412, x = q_1_cast_fp16)[name = tensor("op_413_cast_fp16")]; + tensor var_414 = const()[name = tensor("op_414"), val = tensor([1, 10, 64, -1])]; + tensor var_415_cast_fp16 = reshape(shape = var_414, x = k_1_cast_fp16)[name = tensor("op_415_cast_fp16")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 10, 64, -1])]; + tensor var_417_cast_fp16 = reshape(shape = var_416, x = v_1_cast_fp16)[name = tensor("op_417_cast_fp16")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_413_cast_fp16, y = var_415_cast_fp16)[name = tensor("attn_weights_1_cast_fp16")]; + tensor var_273_to_fp16 = const()[name = tensor("op_273_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_3_cast_fp16")]; + tensor var_421_cast_fp16 = softmax(axis = var_266, x = attn_weights_3_cast_fp16)[name = tensor("op_421_cast_fp16")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_417_cast_fp16, y = var_421_cast_fp16)[name = tensor("attn_1_cast_fp16")]; + tensor var_425 = const()[name = tensor("op_425"), val = tensor([1, 640, 1, -1])]; + tensor input_61_cast_fp16 = reshape(shape = var_425, x = attn_1_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; + tensor var_432 = const()[name = tensor("op_432"), val = tensor([1, 1])]; + tensor var_434_pad_type_0 = const()[name = tensor("op_434_pad_type_0"), val = tensor("custom")]; + tensor var_434_pad_0 = const()[name = tensor("op_434_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15739136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16046400))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16046592)))]; + tensor var_434_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_432, groups = var_282, pad = var_434_pad_0, pad_type = var_434_pad_type_0, strides = var_430, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = tensor("op_434_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = var_434_cast_fp16, y = inputs_1_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; + tensor hidden_states_19_axes_0 = const()[name = tensor("hidden_states_19_axes_0"), val = tensor([1])]; + tensor hidden_states_19_gamma_0_to_fp16 = const()[name = tensor("hidden_states_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16047936)))]; + tensor hidden_states_19_beta_0_to_fp16 = const()[name = tensor("hidden_states_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16049280)))]; + tensor var_444_to_fp16 = const()[name = tensor("op_444_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_19_cast_fp16 = layer_norm(axes = hidden_states_19_axes_0, beta = hidden_states_19_beta_0_to_fp16, epsilon = var_444_to_fp16, gamma = hidden_states_19_gamma_0_to_fp16, x = inputs_3_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor var_459 = const()[name = tensor("op_459"), val = tensor([1, 1])]; + tensor var_461 = const()[name = tensor("op_461"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16050624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16357888))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_3_cast_fp16 = conv(dilations = var_461, groups = var_282, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_459, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_19_cast_fp16)[name = tensor("q_3_cast_fp16")]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16358080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17341184))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_3_cast_fp16 = conv(dilations = var_467, groups = var_282, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_465, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_3_cast_fp16")]; + tensor var_471 = const()[name = tensor("op_471"), val = tensor([1, 1])]; + tensor var_473 = const()[name = tensor("op_473"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17341376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18324480))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_3_cast_fp16 = conv(dilations = var_473, groups = var_282, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_471, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_3_cast_fp16")]; + tensor var_477 = const()[name = tensor("op_477"), val = tensor([1, 10, 64, -1])]; + tensor var_478_cast_fp16 = reshape(shape = var_477, x = q_3_cast_fp16)[name = tensor("op_478_cast_fp16")]; + tensor var_479 = const()[name = tensor("op_479"), val = tensor([1, 10, 64, -1])]; + tensor var_480_cast_fp16 = reshape(shape = var_479, x = k_3_cast_fp16)[name = tensor("op_480_cast_fp16")]; + tensor var_481 = const()[name = tensor("op_481"), val = tensor([1, 10, 64, -1])]; + tensor var_482_cast_fp16 = reshape(shape = var_481, x = v_3_cast_fp16)[name = tensor("op_482_cast_fp16")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast_fp16 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_478_cast_fp16, y = var_480_cast_fp16)[name = tensor("attn_weights_5_cast_fp16")]; + tensor attn_weights_7_cast_fp16 = mul(x = attn_weights_5_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_7_cast_fp16")]; + tensor var_486_cast_fp16 = softmax(axis = var_266, x = attn_weights_7_cast_fp16)[name = tensor("op_486_cast_fp16")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_482_cast_fp16, y = var_486_cast_fp16)[name = tensor("attn_3_cast_fp16")]; + tensor var_490 = const()[name = tensor("op_490"), val = tensor([1, 640, 1, -1])]; + tensor input_63_cast_fp16 = reshape(shape = var_490, x = attn_3_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; + tensor var_499_pad_type_0 = const()[name = tensor("op_499_pad_type_0"), val = tensor("custom")]; + tensor var_499_pad_0 = const()[name = tensor("op_499_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18324672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18631936))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18632128)))]; + tensor var_499_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_497, groups = var_282, pad = var_499_pad_0, pad_type = var_499_pad_type_0, strides = var_495, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("op_499_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = var_499_cast_fp16, y = inputs_3_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; + tensor input_65_axes_0 = const()[name = tensor("input_65_axes_0"), val = tensor([1])]; + tensor input_65_gamma_0_to_fp16 = const()[name = tensor("input_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18633472)))]; + tensor input_65_beta_0_to_fp16 = const()[name = tensor("input_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18634816)))]; + tensor var_509_to_fp16 = const()[name = tensor("op_509_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = input_65_beta_0_to_fp16, epsilon = var_509_to_fp16, gamma = input_65_gamma_0_to_fp16, x = inputs_5_cast_fp16)[name = tensor("input_65_cast_fp16")]; + tensor var_525 = const()[name = tensor("op_525"), val = tensor([1, 1])]; + tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; + tensor var_529_pad_type_0 = const()[name = tensor("op_529_pad_type_0"), val = tensor("custom")]; + tensor var_529_pad_0 = const()[name = tensor("op_529_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18636160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21093824))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21094016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21097920))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_529_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_527, groups = var_282, pad = var_529_pad_0, pad_type = var_529_pad_type_0, strides = var_525, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = tensor("op_529_cast_fp16")]; + tensor var_530_split_sizes_0 = const()[name = tensor("op_530_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_530_axis_0 = const()[name = tensor("op_530_axis_0"), val = tensor(1)]; + tensor var_530_cast_fp16_0, tensor var_530_cast_fp16_1 = split(axis = var_530_axis_0, split_sizes = var_530_split_sizes_0, x = var_529_cast_fp16)[name = tensor("op_530_cast_fp16")]; + tensor var_532_mode_0 = const()[name = tensor("op_532_mode_0"), val = tensor("EXACT")]; + tensor var_532_cast_fp16 = gelu(mode = var_532_mode_0, x = var_530_cast_fp16_1)[name = tensor("op_532_cast_fp16")]; + tensor input_67_cast_fp16 = mul(x = var_530_cast_fp16_0, y = var_532_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor var_536 = const()[name = tensor("op_536"), val = tensor([1, 1])]; + tensor var_538 = const()[name = tensor("op_538"), val = tensor([1, 1])]; + tensor var_540_pad_type_0 = const()[name = tensor("op_540_pad_type_0"), val = tensor("custom")]; + tensor var_540_pad_0 = const()[name = tensor("op_540_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21098112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22326976))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22327168)))]; + tensor var_540_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_538, groups = var_282, pad = var_540_pad_0, pad_type = var_540_pad_type_0, strides = var_536, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = tensor("op_540_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = var_540_cast_fp16, y = inputs_5_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; + tensor hidden_states_23_axes_0 = const()[name = tensor("hidden_states_23_axes_0"), val = tensor([1])]; + tensor hidden_states_23_gamma_0_to_fp16 = const()[name = tensor("hidden_states_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22328512)))]; + tensor hidden_states_23_beta_0_to_fp16 = const()[name = tensor("hidden_states_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22329856)))]; + tensor var_556_to_fp16 = const()[name = tensor("op_556_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_23_cast_fp16 = layer_norm(axes = hidden_states_23_axes_0, beta = hidden_states_23_beta_0_to_fp16, epsilon = var_556_to_fp16, gamma = hidden_states_23_gamma_0_to_fp16, x = inputs_7_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1, 1])]; + tensor var_573 = const()[name = tensor("op_573"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22331200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22638464))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_5_cast_fp16 = conv(dilations = var_573, groups = var_282, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_571, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_23_cast_fp16)[name = tensor("q_5_cast_fp16")]; + tensor var_577 = const()[name = tensor("op_577"), val = tensor([1, 1])]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22638656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22945920))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_5_cast_fp16 = conv(dilations = var_579, groups = var_282, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_577, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_23_cast_fp16)[name = tensor("k_5_cast_fp16")]; + tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, 1])]; + tensor var_585 = const()[name = tensor("op_585"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22946112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23253376))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_5_cast_fp16 = conv(dilations = var_585, groups = var_282, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_583, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_23_cast_fp16)[name = tensor("v_5_cast_fp16")]; + tensor var_589 = const()[name = tensor("op_589"), val = tensor([1, 10, 64, -1])]; + tensor var_590_cast_fp16 = reshape(shape = var_589, x = q_5_cast_fp16)[name = tensor("op_590_cast_fp16")]; + tensor var_591 = const()[name = tensor("op_591"), val = tensor([1, 10, 64, -1])]; + tensor var_592_cast_fp16 = reshape(shape = var_591, x = k_5_cast_fp16)[name = tensor("op_592_cast_fp16")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 10, 64, -1])]; + tensor var_594_cast_fp16 = reshape(shape = var_593, x = v_5_cast_fp16)[name = tensor("op_594_cast_fp16")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_590_cast_fp16, y = var_592_cast_fp16)[name = tensor("attn_weights_9_cast_fp16")]; + tensor attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_11_cast_fp16")]; + tensor var_598_cast_fp16 = softmax(axis = var_266, x = attn_weights_11_cast_fp16)[name = tensor("op_598_cast_fp16")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_594_cast_fp16, y = var_598_cast_fp16)[name = tensor("attn_5_cast_fp16")]; + tensor var_602 = const()[name = tensor("op_602"), val = tensor([1, 640, 1, -1])]; + tensor input_69_cast_fp16 = reshape(shape = var_602, x = attn_5_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor var_607 = const()[name = tensor("op_607"), val = tensor([1, 1])]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611_pad_type_0 = const()[name = tensor("op_611_pad_type_0"), val = tensor("custom")]; + tensor var_611_pad_0 = const()[name = tensor("op_611_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23253568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23560832))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23561024)))]; + tensor var_611_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_609, groups = var_282, pad = var_611_pad_0, pad_type = var_611_pad_type_0, strides = var_607, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor("op_611_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = var_611_cast_fp16, y = inputs_7_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; + tensor hidden_states_25_axes_0 = const()[name = tensor("hidden_states_25_axes_0"), val = tensor([1])]; + tensor hidden_states_25_gamma_0_to_fp16 = const()[name = tensor("hidden_states_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23562368)))]; + tensor hidden_states_25_beta_0_to_fp16 = const()[name = tensor("hidden_states_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23563712)))]; + tensor var_621_to_fp16 = const()[name = tensor("op_621_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_25_cast_fp16 = layer_norm(axes = hidden_states_25_axes_0, beta = hidden_states_25_beta_0_to_fp16, epsilon = var_621_to_fp16, gamma = hidden_states_25_gamma_0_to_fp16, x = inputs_9_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_636 = const()[name = tensor("op_636"), val = tensor([1, 1])]; + tensor var_638 = const()[name = tensor("op_638"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23565056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23872320))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_7_cast_fp16 = conv(dilations = var_638, groups = var_282, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_636, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_25_cast_fp16)[name = tensor("q_7_cast_fp16")]; + tensor var_642 = const()[name = tensor("op_642"), val = tensor([1, 1])]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23872512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24855616))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_7_cast_fp16 = conv(dilations = var_644, groups = var_282, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_642, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_7_cast_fp16")]; + tensor var_648 = const()[name = tensor("op_648"), val = tensor([1, 1])]; + tensor var_650 = const()[name = tensor("op_650"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24855808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25838912))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_7_cast_fp16 = conv(dilations = var_650, groups = var_282, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_648, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_7_cast_fp16")]; + tensor var_654 = const()[name = tensor("op_654"), val = tensor([1, 10, 64, -1])]; + tensor var_655_cast_fp16 = reshape(shape = var_654, x = q_7_cast_fp16)[name = tensor("op_655_cast_fp16")]; + tensor var_656 = const()[name = tensor("op_656"), val = tensor([1, 10, 64, -1])]; + tensor var_657_cast_fp16 = reshape(shape = var_656, x = k_7_cast_fp16)[name = tensor("op_657_cast_fp16")]; + tensor var_658 = const()[name = tensor("op_658"), val = tensor([1, 10, 64, -1])]; + tensor var_659_cast_fp16 = reshape(shape = var_658, x = v_7_cast_fp16)[name = tensor("op_659_cast_fp16")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_655_cast_fp16, y = var_657_cast_fp16)[name = tensor("attn_weights_13_cast_fp16")]; + tensor attn_weights_15_cast_fp16 = mul(x = attn_weights_13_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_15_cast_fp16")]; + tensor var_663_cast_fp16 = softmax(axis = var_266, x = attn_weights_15_cast_fp16)[name = tensor("op_663_cast_fp16")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_659_cast_fp16, y = var_663_cast_fp16)[name = tensor("attn_7_cast_fp16")]; + tensor var_667 = const()[name = tensor("op_667"), val = tensor([1, 640, 1, -1])]; + tensor input_71_cast_fp16 = reshape(shape = var_667, x = attn_7_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; + tensor var_676_pad_type_0 = const()[name = tensor("op_676_pad_type_0"), val = tensor("custom")]; + tensor var_676_pad_0 = const()[name = tensor("op_676_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25839104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26146368))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26146560)))]; + tensor var_676_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_674, groups = var_282, pad = var_676_pad_0, pad_type = var_676_pad_type_0, strides = var_672, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = tensor("op_676_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = var_676_cast_fp16, y = inputs_9_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; + tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([1])]; + tensor input_73_gamma_0_to_fp16 = const()[name = tensor("input_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26147904)))]; + tensor input_73_beta_0_to_fp16 = const()[name = tensor("input_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26149248)))]; + tensor var_686_to_fp16 = const()[name = tensor("op_686_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = input_73_beta_0_to_fp16, epsilon = var_686_to_fp16, gamma = input_73_gamma_0_to_fp16, x = inputs_11_cast_fp16)[name = tensor("input_73_cast_fp16")]; + tensor var_702 = const()[name = tensor("op_702"), val = tensor([1, 1])]; + tensor var_704 = const()[name = tensor("op_704"), val = tensor([1, 1])]; + tensor var_706_pad_type_0 = const()[name = tensor("op_706_pad_type_0"), val = tensor("custom")]; + tensor var_706_pad_0 = const()[name = tensor("op_706_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26150592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28608256))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28608448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28612352))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_706_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_704, groups = var_282, pad = var_706_pad_0, pad_type = var_706_pad_type_0, strides = var_702, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_73_cast_fp16)[name = tensor("op_706_cast_fp16")]; + tensor var_707_split_sizes_0 = const()[name = tensor("op_707_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_707_axis_0 = const()[name = tensor("op_707_axis_0"), val = tensor(1)]; + tensor var_707_cast_fp16_0, tensor var_707_cast_fp16_1 = split(axis = var_707_axis_0, split_sizes = var_707_split_sizes_0, x = var_706_cast_fp16)[name = tensor("op_707_cast_fp16")]; + tensor var_709_mode_0 = const()[name = tensor("op_709_mode_0"), val = tensor("EXACT")]; + tensor var_709_cast_fp16 = gelu(mode = var_709_mode_0, x = var_707_cast_fp16_1)[name = tensor("op_709_cast_fp16")]; + tensor input_75_cast_fp16 = mul(x = var_707_cast_fp16_0, y = var_709_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor var_713 = const()[name = tensor("op_713"), val = tensor([1, 1])]; + tensor var_715 = const()[name = tensor("op_715"), val = tensor([1, 1])]; + tensor var_717_pad_type_0 = const()[name = tensor("op_717_pad_type_0"), val = tensor("custom")]; + tensor var_717_pad_0 = const()[name = tensor("op_717_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28612544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29841408))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29841600)))]; + tensor var_717_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_715, groups = var_282, pad = var_717_pad_0, pad_type = var_717_pad_type_0, strides = var_713, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor("op_717_cast_fp16")]; + tensor hidden_states_29_cast_fp16 = add(x = var_717_cast_fp16, y = inputs_11_cast_fp16)[name = tensor("hidden_states_29_cast_fp16")]; + tensor var_719 = const()[name = tensor("op_719"), val = tensor([1, 640, 64, 64])]; + tensor input_77_cast_fp16 = reshape(shape = var_719, x = hidden_states_29_cast_fp16)[name = tensor("input_77_cast_fp16")]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor var_725 = const()[name = tensor("op_725"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29842944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30150208))), name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30150400)))]; + tensor hidden_states_31_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_725, groups = var_282, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_723, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor input_79_cast_fp16 = add(x = hidden_states_31_cast_fp16, y = hidden_states_13_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_79_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30151744)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30153088)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor input_83_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_83_cast_fp16")]; + tensor var_740 = const()[name = tensor("op_740"), val = tensor([1, 1])]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([1, 1])]; + tensor hidden_states_33_pad_type_0 = const()[name = tensor("hidden_states_33_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_33_pad_0 = const()[name = tensor("hidden_states_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30154432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32919296))), name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32919488)))]; + tensor hidden_states_33_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_742, groups = var_282, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_740, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_748 = const()[name = tensor("op_748"), val = tensor([1, 1])]; + tensor var_750 = const()[name = tensor("op_750"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32920832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33535296))), name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33535488)))]; + tensor temb_7_cast_fp16 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_750, groups = var_282, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_748, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_7_cast_fp16")]; + tensor input_87_cast_fp16 = add(x = hidden_states_33_cast_fp16, y = temb_7_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_87_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33536832)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33538176)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor input_91_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor var_760 = const()[name = tensor("op_760"), val = tensor([1, 1])]; + tensor var_762 = const()[name = tensor("op_762"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33539520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36304384))), name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36304576)))]; + tensor hidden_states_35_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_762, groups = var_282, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_760, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = add(x = input_79_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = hidden_states_37_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36305920)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36307264)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor var_784 = const()[name = tensor("op_784"), val = tensor([1, 1])]; + tensor var_786 = const()[name = tensor("op_786"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36308608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36615872))), name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36616064)))]; + tensor hidden_states_39_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_786, groups = var_282, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_784, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_19_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_791 = const()[name = tensor("op_791"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_13_cast_fp16 = reshape(shape = var_791, x = hidden_states_39_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; + tensor hidden_states_41_axes_0 = const()[name = tensor("hidden_states_41_axes_0"), val = tensor([1])]; + tensor hidden_states_41_gamma_0_to_fp16 = const()[name = tensor("hidden_states_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36617408)))]; + tensor hidden_states_41_beta_0_to_fp16 = const()[name = tensor("hidden_states_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36618752)))]; + tensor var_807_to_fp16 = const()[name = tensor("op_807_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_41_cast_fp16 = layer_norm(axes = hidden_states_41_axes_0, beta = hidden_states_41_beta_0_to_fp16, epsilon = var_807_to_fp16, gamma = hidden_states_41_gamma_0_to_fp16, x = inputs_13_cast_fp16)[name = tensor("hidden_states_41_cast_fp16")]; + tensor var_822 = const()[name = tensor("op_822"), val = tensor([1, 1])]; + tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36620096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36927360))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_9_cast_fp16 = conv(dilations = var_824, groups = var_282, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_822, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_41_cast_fp16)[name = tensor("q_9_cast_fp16")]; + tensor var_828 = const()[name = tensor("op_828"), val = tensor([1, 1])]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36927552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37234816))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_9_cast_fp16 = conv(dilations = var_830, groups = var_282, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_828, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_41_cast_fp16)[name = tensor("k_9_cast_fp16")]; + tensor var_834 = const()[name = tensor("op_834"), val = tensor([1, 1])]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37235008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37542272))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_9_cast_fp16 = conv(dilations = var_836, groups = var_282, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_834, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_41_cast_fp16)[name = tensor("v_9_cast_fp16")]; + tensor var_840 = const()[name = tensor("op_840"), val = tensor([1, 10, 64, -1])]; + tensor var_841_cast_fp16 = reshape(shape = var_840, x = q_9_cast_fp16)[name = tensor("op_841_cast_fp16")]; + tensor var_842 = const()[name = tensor("op_842"), val = tensor([1, 10, 64, -1])]; + tensor var_843_cast_fp16 = reshape(shape = var_842, x = k_9_cast_fp16)[name = tensor("op_843_cast_fp16")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([1, 10, 64, -1])]; + tensor var_845_cast_fp16 = reshape(shape = var_844, x = v_9_cast_fp16)[name = tensor("op_845_cast_fp16")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_841_cast_fp16, y = var_843_cast_fp16)[name = tensor("attn_weights_17_cast_fp16")]; + tensor attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_19_cast_fp16")]; + tensor var_849_cast_fp16 = softmax(axis = var_266, x = attn_weights_19_cast_fp16)[name = tensor("op_849_cast_fp16")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_845_cast_fp16, y = var_849_cast_fp16)[name = tensor("attn_9_cast_fp16")]; + tensor var_853 = const()[name = tensor("op_853"), val = tensor([1, 640, 1, -1])]; + tensor input_95_cast_fp16 = reshape(shape = var_853, x = attn_9_cast_fp16)[name = tensor("input_95_cast_fp16")]; + tensor var_858 = const()[name = tensor("op_858"), val = tensor([1, 1])]; + tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; + tensor var_862_pad_type_0 = const()[name = tensor("op_862_pad_type_0"), val = tensor("custom")]; + tensor var_862_pad_0 = const()[name = tensor("op_862_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37542464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37849728))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37849920)))]; + tensor var_862_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_860, groups = var_282, pad = var_862_pad_0, pad_type = var_862_pad_type_0, strides = var_858, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = tensor("op_862_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = var_862_cast_fp16, y = inputs_13_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; + tensor hidden_states_43_axes_0 = const()[name = tensor("hidden_states_43_axes_0"), val = tensor([1])]; + tensor hidden_states_43_gamma_0_to_fp16 = const()[name = tensor("hidden_states_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37851264)))]; + tensor hidden_states_43_beta_0_to_fp16 = const()[name = tensor("hidden_states_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37852608)))]; + tensor var_872_to_fp16 = const()[name = tensor("op_872_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_43_cast_fp16 = layer_norm(axes = hidden_states_43_axes_0, beta = hidden_states_43_beta_0_to_fp16, epsilon = var_872_to_fp16, gamma = hidden_states_43_gamma_0_to_fp16, x = inputs_15_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_887 = const()[name = tensor("op_887"), val = tensor([1, 1])]; + tensor var_889 = const()[name = tensor("op_889"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37853952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38161216))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_11_cast_fp16 = conv(dilations = var_889, groups = var_282, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_887, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_43_cast_fp16)[name = tensor("q_11_cast_fp16")]; + tensor var_893 = const()[name = tensor("op_893"), val = tensor([1, 1])]; + tensor var_895 = const()[name = tensor("op_895"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38161408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39144512))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_11_cast_fp16 = conv(dilations = var_895, groups = var_282, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_893, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_11_cast_fp16")]; + tensor var_899 = const()[name = tensor("op_899"), val = tensor([1, 1])]; + tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39144704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40127808))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_11_cast_fp16 = conv(dilations = var_901, groups = var_282, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_899, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_11_cast_fp16")]; + tensor var_905 = const()[name = tensor("op_905"), val = tensor([1, 10, 64, -1])]; + tensor var_906_cast_fp16 = reshape(shape = var_905, x = q_11_cast_fp16)[name = tensor("op_906_cast_fp16")]; + tensor var_907 = const()[name = tensor("op_907"), val = tensor([1, 10, 64, -1])]; + tensor var_908_cast_fp16 = reshape(shape = var_907, x = k_11_cast_fp16)[name = tensor("op_908_cast_fp16")]; + tensor var_909 = const()[name = tensor("op_909"), val = tensor([1, 10, 64, -1])]; + tensor var_910_cast_fp16 = reshape(shape = var_909, x = v_11_cast_fp16)[name = tensor("op_910_cast_fp16")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast_fp16 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_906_cast_fp16, y = var_908_cast_fp16)[name = tensor("attn_weights_21_cast_fp16")]; + tensor attn_weights_23_cast_fp16 = mul(x = attn_weights_21_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_23_cast_fp16")]; + tensor var_914_cast_fp16 = softmax(axis = var_266, x = attn_weights_23_cast_fp16)[name = tensor("op_914_cast_fp16")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_910_cast_fp16, y = var_914_cast_fp16)[name = tensor("attn_11_cast_fp16")]; + tensor var_918 = const()[name = tensor("op_918"), val = tensor([1, 640, 1, -1])]; + tensor input_97_cast_fp16 = reshape(shape = var_918, x = attn_11_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor var_923 = const()[name = tensor("op_923"), val = tensor([1, 1])]; + tensor var_925 = const()[name = tensor("op_925"), val = tensor([1, 1])]; + tensor var_927_pad_type_0 = const()[name = tensor("op_927_pad_type_0"), val = tensor("custom")]; + tensor var_927_pad_0 = const()[name = tensor("op_927_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40128000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40435264))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40435456)))]; + tensor var_927_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_925, groups = var_282, pad = var_927_pad_0, pad_type = var_927_pad_type_0, strides = var_923, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_97_cast_fp16)[name = tensor("op_927_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = var_927_cast_fp16, y = inputs_15_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; + tensor input_99_axes_0 = const()[name = tensor("input_99_axes_0"), val = tensor([1])]; + tensor input_99_gamma_0_to_fp16 = const()[name = tensor("input_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40436800)))]; + tensor input_99_beta_0_to_fp16 = const()[name = tensor("input_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40438144)))]; + tensor var_937_to_fp16 = const()[name = tensor("op_937_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_99_cast_fp16 = layer_norm(axes = input_99_axes_0, beta = input_99_beta_0_to_fp16, epsilon = var_937_to_fp16, gamma = input_99_gamma_0_to_fp16, x = inputs_17_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor var_953 = const()[name = tensor("op_953"), val = tensor([1, 1])]; + tensor var_955 = const()[name = tensor("op_955"), val = tensor([1, 1])]; + tensor var_957_pad_type_0 = const()[name = tensor("op_957_pad_type_0"), val = tensor("custom")]; + tensor var_957_pad_0 = const()[name = tensor("op_957_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40439488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42897152))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42897344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42901248))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_957_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_955, groups = var_282, pad = var_957_pad_0, pad_type = var_957_pad_type_0, strides = var_953, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("op_957_cast_fp16")]; + tensor var_958_split_sizes_0 = const()[name = tensor("op_958_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_958_axis_0 = const()[name = tensor("op_958_axis_0"), val = tensor(1)]; + tensor var_958_cast_fp16_0, tensor var_958_cast_fp16_1 = split(axis = var_958_axis_0, split_sizes = var_958_split_sizes_0, x = var_957_cast_fp16)[name = tensor("op_958_cast_fp16")]; + tensor var_960_mode_0 = const()[name = tensor("op_960_mode_0"), val = tensor("EXACT")]; + tensor var_960_cast_fp16 = gelu(mode = var_960_mode_0, x = var_958_cast_fp16_1)[name = tensor("op_960_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = var_958_cast_fp16_0, y = var_960_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor var_964 = const()[name = tensor("op_964"), val = tensor([1, 1])]; + tensor var_966 = const()[name = tensor("op_966"), val = tensor([1, 1])]; + tensor var_968_pad_type_0 = const()[name = tensor("op_968_pad_type_0"), val = tensor("custom")]; + tensor var_968_pad_0 = const()[name = tensor("op_968_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42901440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44130304))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44130496)))]; + tensor var_968_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_966, groups = var_282, pad = var_968_pad_0, pad_type = var_968_pad_type_0, strides = var_964, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = tensor("op_968_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = var_968_cast_fp16, y = inputs_17_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; + tensor hidden_states_47_axes_0 = const()[name = tensor("hidden_states_47_axes_0"), val = tensor([1])]; + tensor hidden_states_47_gamma_0_to_fp16 = const()[name = tensor("hidden_states_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44131840)))]; + tensor hidden_states_47_beta_0_to_fp16 = const()[name = tensor("hidden_states_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44133184)))]; + tensor var_984_to_fp16 = const()[name = tensor("op_984_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_47_cast_fp16 = layer_norm(axes = hidden_states_47_axes_0, beta = hidden_states_47_beta_0_to_fp16, epsilon = var_984_to_fp16, gamma = hidden_states_47_gamma_0_to_fp16, x = inputs_19_cast_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_999 = const()[name = tensor("op_999"), val = tensor([1, 1])]; + tensor var_1001 = const()[name = tensor("op_1001"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44134528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44441792))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_13_cast_fp16 = conv(dilations = var_1001, groups = var_282, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_999, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("q_13_cast_fp16")]; + tensor var_1005 = const()[name = tensor("op_1005"), val = tensor([1, 1])]; + tensor var_1007 = const()[name = tensor("op_1007"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44441984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44749248))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_13_cast_fp16 = conv(dilations = var_1007, groups = var_282, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1005, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("k_13_cast_fp16")]; + tensor var_1011 = const()[name = tensor("op_1011"), val = tensor([1, 1])]; + tensor var_1013 = const()[name = tensor("op_1013"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44749440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45056704))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_13_cast_fp16 = conv(dilations = var_1013, groups = var_282, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1011, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_47_cast_fp16)[name = tensor("v_13_cast_fp16")]; + tensor var_1017 = const()[name = tensor("op_1017"), val = tensor([1, 10, 64, -1])]; + tensor var_1018_cast_fp16 = reshape(shape = var_1017, x = q_13_cast_fp16)[name = tensor("op_1018_cast_fp16")]; + tensor var_1019 = const()[name = tensor("op_1019"), val = tensor([1, 10, 64, -1])]; + tensor var_1020_cast_fp16 = reshape(shape = var_1019, x = k_13_cast_fp16)[name = tensor("op_1020_cast_fp16")]; + tensor var_1021 = const()[name = tensor("op_1021"), val = tensor([1, 10, 64, -1])]; + tensor var_1022_cast_fp16 = reshape(shape = var_1021, x = v_13_cast_fp16)[name = tensor("op_1022_cast_fp16")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1018_cast_fp16, y = var_1020_cast_fp16)[name = tensor("attn_weights_25_cast_fp16")]; + tensor attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_27_cast_fp16")]; + tensor var_1026_cast_fp16 = softmax(axis = var_266, x = attn_weights_27_cast_fp16)[name = tensor("op_1026_cast_fp16")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1022_cast_fp16, y = var_1026_cast_fp16)[name = tensor("attn_13_cast_fp16")]; + tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([1, 640, 1, -1])]; + tensor input_103_cast_fp16 = reshape(shape = var_1030, x = attn_13_cast_fp16)[name = tensor("input_103_cast_fp16")]; + tensor var_1035 = const()[name = tensor("op_1035"), val = tensor([1, 1])]; + tensor var_1037 = const()[name = tensor("op_1037"), val = tensor([1, 1])]; + tensor var_1039_pad_type_0 = const()[name = tensor("op_1039_pad_type_0"), val = tensor("custom")]; + tensor var_1039_pad_0 = const()[name = tensor("op_1039_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45056896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45364160))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45364352)))]; + tensor var_1039_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1037, groups = var_282, pad = var_1039_pad_0, pad_type = var_1039_pad_type_0, strides = var_1035, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_103_cast_fp16)[name = tensor("op_1039_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = var_1039_cast_fp16, y = inputs_19_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; + tensor hidden_states_49_axes_0 = const()[name = tensor("hidden_states_49_axes_0"), val = tensor([1])]; + tensor hidden_states_49_gamma_0_to_fp16 = const()[name = tensor("hidden_states_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45365696)))]; + tensor hidden_states_49_beta_0_to_fp16 = const()[name = tensor("hidden_states_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45367040)))]; + tensor var_1049_to_fp16 = const()[name = tensor("op_1049_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_49_cast_fp16 = layer_norm(axes = hidden_states_49_axes_0, beta = hidden_states_49_beta_0_to_fp16, epsilon = var_1049_to_fp16, gamma = hidden_states_49_gamma_0_to_fp16, x = inputs_21_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor var_1064 = const()[name = tensor("op_1064"), val = tensor([1, 1])]; + tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45368384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45675648))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_15_cast_fp16 = conv(dilations = var_1066, groups = var_282, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1064, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_49_cast_fp16)[name = tensor("q_15_cast_fp16")]; + tensor var_1070 = const()[name = tensor("op_1070"), val = tensor([1, 1])]; + tensor var_1072 = const()[name = tensor("op_1072"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45675840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46658944))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_15_cast_fp16 = conv(dilations = var_1072, groups = var_282, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1070, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_15_cast_fp16")]; + tensor var_1076 = const()[name = tensor("op_1076"), val = tensor([1, 1])]; + tensor var_1078 = const()[name = tensor("op_1078"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46659136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47642240))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_15_cast_fp16 = conv(dilations = var_1078, groups = var_282, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1076, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_15_cast_fp16")]; + tensor var_1082 = const()[name = tensor("op_1082"), val = tensor([1, 10, 64, -1])]; + tensor var_1083_cast_fp16 = reshape(shape = var_1082, x = q_15_cast_fp16)[name = tensor("op_1083_cast_fp16")]; + tensor var_1084 = const()[name = tensor("op_1084"), val = tensor([1, 10, 64, -1])]; + tensor var_1085_cast_fp16 = reshape(shape = var_1084, x = k_15_cast_fp16)[name = tensor("op_1085_cast_fp16")]; + tensor var_1086 = const()[name = tensor("op_1086"), val = tensor([1, 10, 64, -1])]; + tensor var_1087_cast_fp16 = reshape(shape = var_1086, x = v_15_cast_fp16)[name = tensor("op_1087_cast_fp16")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast_fp16 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1083_cast_fp16, y = var_1085_cast_fp16)[name = tensor("attn_weights_29_cast_fp16")]; + tensor attn_weights_31_cast_fp16 = mul(x = attn_weights_29_cast_fp16, y = var_273_to_fp16)[name = tensor("attn_weights_31_cast_fp16")]; + tensor var_1091_cast_fp16 = softmax(axis = var_266, x = attn_weights_31_cast_fp16)[name = tensor("op_1091_cast_fp16")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1087_cast_fp16, y = var_1091_cast_fp16)[name = tensor("attn_15_cast_fp16")]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([1, 640, 1, -1])]; + tensor input_105_cast_fp16 = reshape(shape = var_1095, x = attn_15_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor var_1100 = const()[name = tensor("op_1100"), val = tensor([1, 1])]; + tensor var_1102 = const()[name = tensor("op_1102"), val = tensor([1, 1])]; + tensor var_1104_pad_type_0 = const()[name = tensor("op_1104_pad_type_0"), val = tensor("custom")]; + tensor var_1104_pad_0 = const()[name = tensor("op_1104_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47642432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47949696))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47949888)))]; + tensor var_1104_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1102, groups = var_282, pad = var_1104_pad_0, pad_type = var_1104_pad_type_0, strides = var_1100, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = tensor("op_1104_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = var_1104_cast_fp16, y = inputs_21_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; + tensor input_107_axes_0 = const()[name = tensor("input_107_axes_0"), val = tensor([1])]; + tensor input_107_gamma_0_to_fp16 = const()[name = tensor("input_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47951232)))]; + tensor input_107_beta_0_to_fp16 = const()[name = tensor("input_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47952576)))]; + tensor var_1114_to_fp16 = const()[name = tensor("op_1114_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_107_cast_fp16 = layer_norm(axes = input_107_axes_0, beta = input_107_beta_0_to_fp16, epsilon = var_1114_to_fp16, gamma = input_107_gamma_0_to_fp16, x = inputs_23_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor var_1130 = const()[name = tensor("op_1130"), val = tensor([1, 1])]; + tensor var_1132 = const()[name = tensor("op_1132"), val = tensor([1, 1])]; + tensor var_1134_pad_type_0 = const()[name = tensor("op_1134_pad_type_0"), val = tensor("custom")]; + tensor var_1134_pad_0 = const()[name = tensor("op_1134_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47953920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50411584))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50411776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50415680))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_1134_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1132, groups = var_282, pad = var_1134_pad_0, pad_type = var_1134_pad_type_0, strides = var_1130, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor("op_1134_cast_fp16")]; + tensor var_1135_split_sizes_0 = const()[name = tensor("op_1135_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1135_axis_0 = const()[name = tensor("op_1135_axis_0"), val = tensor(1)]; + tensor var_1135_cast_fp16_0, tensor var_1135_cast_fp16_1 = split(axis = var_1135_axis_0, split_sizes = var_1135_split_sizes_0, x = var_1134_cast_fp16)[name = tensor("op_1135_cast_fp16")]; + tensor var_1137_mode_0 = const()[name = tensor("op_1137_mode_0"), val = tensor("EXACT")]; + tensor var_1137_cast_fp16 = gelu(mode = var_1137_mode_0, x = var_1135_cast_fp16_1)[name = tensor("op_1137_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = var_1135_cast_fp16_0, y = var_1137_cast_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; + tensor var_1143 = const()[name = tensor("op_1143"), val = tensor([1, 1])]; + tensor var_1145_pad_type_0 = const()[name = tensor("op_1145_pad_type_0"), val = tensor("custom")]; + tensor var_1145_pad_0 = const()[name = tensor("op_1145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50415872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51644736))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51644928)))]; + tensor var_1145_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1143, groups = var_282, pad = var_1145_pad_0, pad_type = var_1145_pad_type_0, strides = var_1141, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_109_cast_fp16)[name = tensor("op_1145_cast_fp16")]; + tensor hidden_states_53_cast_fp16 = add(x = var_1145_cast_fp16, y = inputs_23_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor var_1147 = const()[name = tensor("op_1147"), val = tensor([1, 640, 64, 64])]; + tensor input_111_cast_fp16 = reshape(shape = var_1147, x = hidden_states_53_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor var_1151 = const()[name = tensor("op_1151"), val = tensor([1, 1])]; + tensor var_1153 = const()[name = tensor("op_1153"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51646272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51953536))), name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51953728)))]; + tensor hidden_states_55_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1153, groups = var_282, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1151, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor input_113_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([2, 2])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; + tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51955072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54719936))), name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54720128)))]; + tensor input_115_cast_fp16 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1162, groups = var_282, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_1160, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor var_1170 = const()[name = tensor("op_1170"), val = tensor(3)]; + tensor var_1186 = const()[name = tensor("op_1186"), val = tensor(1)]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 20, 32, 32])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_115_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 640, 32, 32])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54721472)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54722816)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_119_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor var_1207 = const()[name = tensor("op_1207"), val = tensor([1, 1])]; + tensor var_1209 = const()[name = tensor("op_1209"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54724160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60253824))), name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 640, 3, 3])]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60254016)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1209, groups = var_1186, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1207, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = tensor("hidden_states_57_cast_fp16")]; + tensor var_1215 = const()[name = tensor("op_1215"), val = tensor([1, 1])]; + tensor var_1217 = const()[name = tensor("op_1217"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60256640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61485504))), name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61485696)))]; + tensor temb_9_cast_fp16 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1217, groups = var_1186, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1215, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_9_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = hidden_states_57_cast_fp16, y = temb_9_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_123_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_mean_0_to_fp16 = const()[name = tensor("add_23_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61488320)))]; + tensor add_23_variance_0_to_fp16 = const()[name = tensor("add_23_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61490944)))]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61493568)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61496192)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor input_127_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor("input_127_cast_fp16")]; + tensor var_1227 = const()[name = tensor("op_1227"), val = tensor([1, 1])]; + tensor var_1229 = const()[name = tensor("op_1229"), val = tensor([1, 1])]; + tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61498816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72558080))), name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72558272)))]; + tensor hidden_states_59_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1229, groups = var_1186, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_1227, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_127_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor var_1234 = const()[name = tensor("op_1234"), val = tensor([1, 1])]; + tensor var_1236 = const()[name = tensor("op_1236"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72560896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73175360))), name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 640, 1, 1])]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73175552)))]; + tensor x_3_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1236, groups = var_1186, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1234, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor("x_3_cast_fp16")]; + tensor hidden_states_61_cast_fp16 = add(x = x_3_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = hidden_states_61_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73178176)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73180800)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([1, 1])]; + tensor var_1276 = const()[name = tensor("op_1276"), val = tensor([1, 1])]; + tensor hidden_states_63_pad_type_0 = const()[name = tensor("hidden_states_63_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_63_pad_0 = const()[name = tensor("hidden_states_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73183424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74412288))), name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74412480)))]; + tensor hidden_states_63_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1276, groups = var_1186, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_1274, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_25_cast_fp16)[name = tensor("hidden_states_63_cast_fp16")]; + tensor var_1281 = const()[name = tensor("op_1281"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_25_cast_fp16 = reshape(shape = var_1281, x = hidden_states_63_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; + tensor hidden_states_65_axes_0 = const()[name = tensor("hidden_states_65_axes_0"), val = tensor([1])]; + tensor hidden_states_65_gamma_0_to_fp16 = const()[name = tensor("hidden_states_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74415104)))]; + tensor hidden_states_65_beta_0_to_fp16 = const()[name = tensor("hidden_states_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74417728)))]; + tensor var_1297_to_fp16 = const()[name = tensor("op_1297_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_65_cast_fp16 = layer_norm(axes = hidden_states_65_axes_0, beta = hidden_states_65_beta_0_to_fp16, epsilon = var_1297_to_fp16, gamma = hidden_states_65_gamma_0_to_fp16, x = inputs_25_cast_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_1312 = const()[name = tensor("op_1312"), val = tensor([1, 1])]; + tensor var_1314 = const()[name = tensor("op_1314"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74420352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75649216))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_17_cast_fp16 = conv(dilations = var_1314, groups = var_1186, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1312, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("q_17_cast_fp16")]; + tensor var_1318 = const()[name = tensor("op_1318"), val = tensor([1, 1])]; + tensor var_1320 = const()[name = tensor("op_1320"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75649408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76878272))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_17_cast_fp16 = conv(dilations = var_1320, groups = var_1186, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1318, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("k_17_cast_fp16")]; + tensor var_1324 = const()[name = tensor("op_1324"), val = tensor([1, 1])]; + tensor var_1326 = const()[name = tensor("op_1326"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76878464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78107328))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_17_cast_fp16 = conv(dilations = var_1326, groups = var_1186, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1324, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_65_cast_fp16)[name = tensor("v_17_cast_fp16")]; + tensor var_1330 = const()[name = tensor("op_1330"), val = tensor([1, 20, 64, -1])]; + tensor var_1331_cast_fp16 = reshape(shape = var_1330, x = q_17_cast_fp16)[name = tensor("op_1331_cast_fp16")]; + tensor var_1332 = const()[name = tensor("op_1332"), val = tensor([1, 20, 64, -1])]; + tensor var_1333_cast_fp16 = reshape(shape = var_1332, x = k_17_cast_fp16)[name = tensor("op_1333_cast_fp16")]; + tensor var_1334 = const()[name = tensor("op_1334"), val = tensor([1, 20, 64, -1])]; + tensor var_1335_cast_fp16 = reshape(shape = var_1334, x = v_17_cast_fp16)[name = tensor("op_1335_cast_fp16")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1331_cast_fp16, y = var_1333_cast_fp16)[name = tensor("attn_weights_33_cast_fp16")]; + tensor var_1177_to_fp16 = const()[name = tensor("op_1177_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_35_cast_fp16")]; + tensor var_1339_cast_fp16 = softmax(axis = var_1170, x = attn_weights_35_cast_fp16)[name = tensor("op_1339_cast_fp16")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1335_cast_fp16, y = var_1339_cast_fp16)[name = tensor("attn_17_cast_fp16")]; + tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([1, 1280, 1, -1])]; + tensor input_131_cast_fp16 = reshape(shape = var_1343, x = attn_17_cast_fp16)[name = tensor("input_131_cast_fp16")]; + tensor var_1348 = const()[name = tensor("op_1348"), val = tensor([1, 1])]; + tensor var_1350 = const()[name = tensor("op_1350"), val = tensor([1, 1])]; + tensor var_1352_pad_type_0 = const()[name = tensor("op_1352_pad_type_0"), val = tensor("custom")]; + tensor var_1352_pad_0 = const()[name = tensor("op_1352_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78107520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79336384))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79336576)))]; + tensor var_1352_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1350, groups = var_1186, pad = var_1352_pad_0, pad_type = var_1352_pad_type_0, strides = var_1348, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = tensor("op_1352_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = var_1352_cast_fp16, y = inputs_25_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; + tensor hidden_states_67_axes_0 = const()[name = tensor("hidden_states_67_axes_0"), val = tensor([1])]; + tensor hidden_states_67_gamma_0_to_fp16 = const()[name = tensor("hidden_states_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79339200)))]; + tensor hidden_states_67_beta_0_to_fp16 = const()[name = tensor("hidden_states_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79341824)))]; + tensor var_1362_to_fp16 = const()[name = tensor("op_1362_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_67_cast_fp16 = layer_norm(axes = hidden_states_67_axes_0, beta = hidden_states_67_beta_0_to_fp16, epsilon = var_1362_to_fp16, gamma = hidden_states_67_gamma_0_to_fp16, x = inputs_27_cast_fp16)[name = tensor("hidden_states_67_cast_fp16")]; + tensor var_1377 = const()[name = tensor("op_1377"), val = tensor([1, 1])]; + tensor var_1379 = const()[name = tensor("op_1379"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79344448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80573312))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_19_cast_fp16 = conv(dilations = var_1379, groups = var_1186, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1377, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_67_cast_fp16)[name = tensor("q_19_cast_fp16")]; + tensor var_1383 = const()[name = tensor("op_1383"), val = tensor([1, 1])]; + tensor var_1385 = const()[name = tensor("op_1385"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80573504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_19_cast_fp16 = conv(dilations = var_1385, groups = var_1186, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1383, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_19_cast_fp16")]; + tensor var_1389 = const()[name = tensor("op_1389"), val = tensor([1, 1])]; + tensor var_1391 = const()[name = tensor("op_1391"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82539840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84505984))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_19_cast_fp16 = conv(dilations = var_1391, groups = var_1186, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1389, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_19_cast_fp16")]; + tensor var_1395 = const()[name = tensor("op_1395"), val = tensor([1, 20, 64, -1])]; + tensor var_1396_cast_fp16 = reshape(shape = var_1395, x = q_19_cast_fp16)[name = tensor("op_1396_cast_fp16")]; + tensor var_1397 = const()[name = tensor("op_1397"), val = tensor([1, 20, 64, -1])]; + tensor var_1398_cast_fp16 = reshape(shape = var_1397, x = k_19_cast_fp16)[name = tensor("op_1398_cast_fp16")]; + tensor var_1399 = const()[name = tensor("op_1399"), val = tensor([1, 20, 64, -1])]; + tensor var_1400_cast_fp16 = reshape(shape = var_1399, x = v_19_cast_fp16)[name = tensor("op_1400_cast_fp16")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1396_cast_fp16, y = var_1398_cast_fp16)[name = tensor("attn_weights_37_cast_fp16")]; + tensor attn_weights_39_cast_fp16 = mul(x = attn_weights_37_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_39_cast_fp16")]; + tensor var_1404_cast_fp16 = softmax(axis = var_1170, x = attn_weights_39_cast_fp16)[name = tensor("op_1404_cast_fp16")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1400_cast_fp16, y = var_1404_cast_fp16)[name = tensor("attn_19_cast_fp16")]; + tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1280, 1, -1])]; + tensor input_133_cast_fp16 = reshape(shape = var_1408, x = attn_19_cast_fp16)[name = tensor("input_133_cast_fp16")]; + tensor var_1413 = const()[name = tensor("op_1413"), val = tensor([1, 1])]; + tensor var_1415 = const()[name = tensor("op_1415"), val = tensor([1, 1])]; + tensor var_1417_pad_type_0 = const()[name = tensor("op_1417_pad_type_0"), val = tensor("custom")]; + tensor var_1417_pad_0 = const()[name = tensor("op_1417_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84506176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85735040))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85735232)))]; + tensor var_1417_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1415, groups = var_1186, pad = var_1417_pad_0, pad_type = var_1417_pad_type_0, strides = var_1413, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = tensor("op_1417_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = var_1417_cast_fp16, y = inputs_27_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; + tensor input_135_axes_0 = const()[name = tensor("input_135_axes_0"), val = tensor([1])]; + tensor input_135_gamma_0_to_fp16 = const()[name = tensor("input_135_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85737856)))]; + tensor input_135_beta_0_to_fp16 = const()[name = tensor("input_135_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85740480)))]; + tensor var_1427_to_fp16 = const()[name = tensor("op_1427_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_135_cast_fp16 = layer_norm(axes = input_135_axes_0, beta = input_135_beta_0_to_fp16, epsilon = var_1427_to_fp16, gamma = input_135_gamma_0_to_fp16, x = inputs_29_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor var_1443 = const()[name = tensor("op_1443"), val = tensor([1, 1])]; + tensor var_1445 = const()[name = tensor("op_1445"), val = tensor([1, 1])]; + tensor var_1447_pad_type_0 = const()[name = tensor("op_1447_pad_type_0"), val = tensor("custom")]; + tensor var_1447_pad_0 = const()[name = tensor("op_1447_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85743104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95573568))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95573760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95581504))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1447_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1445, groups = var_1186, pad = var_1447_pad_0, pad_type = var_1447_pad_type_0, strides = var_1443, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = tensor("op_1447_cast_fp16")]; + tensor var_1448_split_sizes_0 = const()[name = tensor("op_1448_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1448_axis_0 = const()[name = tensor("op_1448_axis_0"), val = tensor(1)]; + tensor var_1448_cast_fp16_0, tensor var_1448_cast_fp16_1 = split(axis = var_1448_axis_0, split_sizes = var_1448_split_sizes_0, x = var_1447_cast_fp16)[name = tensor("op_1448_cast_fp16")]; + tensor var_1450_mode_0 = const()[name = tensor("op_1450_mode_0"), val = tensor("EXACT")]; + tensor var_1450_cast_fp16 = gelu(mode = var_1450_mode_0, x = var_1448_cast_fp16_1)[name = tensor("op_1450_cast_fp16")]; + tensor input_137_cast_fp16 = mul(x = var_1448_cast_fp16_0, y = var_1450_cast_fp16)[name = tensor("input_137_cast_fp16")]; + tensor var_1454 = const()[name = tensor("op_1454"), val = tensor([1, 1])]; + tensor var_1456 = const()[name = tensor("op_1456"), val = tensor([1, 1])]; + tensor var_1458_pad_type_0 = const()[name = tensor("op_1458_pad_type_0"), val = tensor("custom")]; + tensor var_1458_pad_0 = const()[name = tensor("op_1458_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95581696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100496960))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100497152)))]; + tensor var_1458_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1456, groups = var_1186, pad = var_1458_pad_0, pad_type = var_1458_pad_type_0, strides = var_1454, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor("op_1458_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = var_1458_cast_fp16, y = inputs_29_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; + tensor hidden_states_71_axes_0 = const()[name = tensor("hidden_states_71_axes_0"), val = tensor([1])]; + tensor hidden_states_71_gamma_0_to_fp16 = const()[name = tensor("hidden_states_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100499776)))]; + tensor hidden_states_71_beta_0_to_fp16 = const()[name = tensor("hidden_states_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100502400)))]; + tensor var_1474_to_fp16 = const()[name = tensor("op_1474_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_71_cast_fp16 = layer_norm(axes = hidden_states_71_axes_0, beta = hidden_states_71_beta_0_to_fp16, epsilon = var_1474_to_fp16, gamma = hidden_states_71_gamma_0_to_fp16, x = inputs_31_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor var_1489 = const()[name = tensor("op_1489"), val = tensor([1, 1])]; + tensor var_1491 = const()[name = tensor("op_1491"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100505024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101733888))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_21_cast_fp16 = conv(dilations = var_1491, groups = var_1186, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1489, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_71_cast_fp16)[name = tensor("q_21_cast_fp16")]; + tensor var_1495 = const()[name = tensor("op_1495"), val = tensor([1, 1])]; + tensor var_1497 = const()[name = tensor("op_1497"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101734080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102962944))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_21_cast_fp16 = conv(dilations = var_1497, groups = var_1186, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1495, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_71_cast_fp16)[name = tensor("k_21_cast_fp16")]; + tensor var_1501 = const()[name = tensor("op_1501"), val = tensor([1, 1])]; + tensor var_1503 = const()[name = tensor("op_1503"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102963136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104192000))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_21_cast_fp16 = conv(dilations = var_1503, groups = var_1186, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1501, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_71_cast_fp16)[name = tensor("v_21_cast_fp16")]; + tensor var_1507 = const()[name = tensor("op_1507"), val = tensor([1, 20, 64, -1])]; + tensor var_1508_cast_fp16 = reshape(shape = var_1507, x = q_21_cast_fp16)[name = tensor("op_1508_cast_fp16")]; + tensor var_1509 = const()[name = tensor("op_1509"), val = tensor([1, 20, 64, -1])]; + tensor var_1510_cast_fp16 = reshape(shape = var_1509, x = k_21_cast_fp16)[name = tensor("op_1510_cast_fp16")]; + tensor var_1511 = const()[name = tensor("op_1511"), val = tensor([1, 20, 64, -1])]; + tensor var_1512_cast_fp16 = reshape(shape = var_1511, x = v_21_cast_fp16)[name = tensor("op_1512_cast_fp16")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1508_cast_fp16, y = var_1510_cast_fp16)[name = tensor("attn_weights_41_cast_fp16")]; + tensor attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_43_cast_fp16")]; + tensor var_1516_cast_fp16 = softmax(axis = var_1170, x = attn_weights_43_cast_fp16)[name = tensor("op_1516_cast_fp16")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1512_cast_fp16, y = var_1516_cast_fp16)[name = tensor("attn_21_cast_fp16")]; + tensor var_1520 = const()[name = tensor("op_1520"), val = tensor([1, 1280, 1, -1])]; + tensor input_139_cast_fp16 = reshape(shape = var_1520, x = attn_21_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_1525 = const()[name = tensor("op_1525"), val = tensor([1, 1])]; + tensor var_1527 = const()[name = tensor("op_1527"), val = tensor([1, 1])]; + tensor var_1529_pad_type_0 = const()[name = tensor("op_1529_pad_type_0"), val = tensor("custom")]; + tensor var_1529_pad_0 = const()[name = tensor("op_1529_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104192192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105421056))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105421248)))]; + tensor var_1529_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1527, groups = var_1186, pad = var_1529_pad_0, pad_type = var_1529_pad_type_0, strides = var_1525, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor("op_1529_cast_fp16")]; + tensor inputs_33_cast_fp16 = add(x = var_1529_cast_fp16, y = inputs_31_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; + tensor hidden_states_73_axes_0 = const()[name = tensor("hidden_states_73_axes_0"), val = tensor([1])]; + tensor hidden_states_73_gamma_0_to_fp16 = const()[name = tensor("hidden_states_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105423872)))]; + tensor hidden_states_73_beta_0_to_fp16 = const()[name = tensor("hidden_states_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105426496)))]; + tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_73_cast_fp16 = layer_norm(axes = hidden_states_73_axes_0, beta = hidden_states_73_beta_0_to_fp16, epsilon = var_1539_to_fp16, gamma = hidden_states_73_gamma_0_to_fp16, x = inputs_33_cast_fp16)[name = tensor("hidden_states_73_cast_fp16")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105429120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106657984))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_23_cast_fp16 = conv(dilations = var_1556, groups = var_1186, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_73_cast_fp16)[name = tensor("q_23_cast_fp16")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106658176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108624320))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_23_cast_fp16 = conv(dilations = var_1562, groups = var_1186, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_23_cast_fp16")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108624512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110590656))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_23_cast_fp16 = conv(dilations = var_1568, groups = var_1186, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_23_cast_fp16")]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([1, 20, 64, -1])]; + tensor var_1573_cast_fp16 = reshape(shape = var_1572, x = q_23_cast_fp16)[name = tensor("op_1573_cast_fp16")]; + tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([1, 20, 64, -1])]; + tensor var_1575_cast_fp16 = reshape(shape = var_1574, x = k_23_cast_fp16)[name = tensor("op_1575_cast_fp16")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([1, 20, 64, -1])]; + tensor var_1577_cast_fp16 = reshape(shape = var_1576, x = v_23_cast_fp16)[name = tensor("op_1577_cast_fp16")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast_fp16 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1573_cast_fp16, y = var_1575_cast_fp16)[name = tensor("attn_weights_45_cast_fp16")]; + tensor attn_weights_47_cast_fp16 = mul(x = attn_weights_45_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_47_cast_fp16")]; + tensor var_1581_cast_fp16 = softmax(axis = var_1170, x = attn_weights_47_cast_fp16)[name = tensor("op_1581_cast_fp16")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1577_cast_fp16, y = var_1581_cast_fp16)[name = tensor("attn_23_cast_fp16")]; + tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([1, 1280, 1, -1])]; + tensor input_141_cast_fp16 = reshape(shape = var_1585, x = attn_23_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; + tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; + tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110590848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111819712))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111819904)))]; + tensor var_1594_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1186, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_141_cast_fp16)[name = tensor("op_1594_cast_fp16")]; + tensor inputs_35_cast_fp16 = add(x = var_1594_cast_fp16, y = inputs_33_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; + tensor input_143_axes_0 = const()[name = tensor("input_143_axes_0"), val = tensor([1])]; + tensor input_143_gamma_0_to_fp16 = const()[name = tensor("input_143_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111822528)))]; + tensor input_143_beta_0_to_fp16 = const()[name = tensor("input_143_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111825152)))]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_143_cast_fp16 = layer_norm(axes = input_143_axes_0, beta = input_143_beta_0_to_fp16, epsilon = var_1604_to_fp16, gamma = input_143_gamma_0_to_fp16, x = inputs_35_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor var_1620 = const()[name = tensor("op_1620"), val = tensor([1, 1])]; + tensor var_1622 = const()[name = tensor("op_1622"), val = tensor([1, 1])]; + tensor var_1624_pad_type_0 = const()[name = tensor("op_1624_pad_type_0"), val = tensor("custom")]; + tensor var_1624_pad_0 = const()[name = tensor("op_1624_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111827776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121658240))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121658432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121666176))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1624_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1622, groups = var_1186, pad = var_1624_pad_0, pad_type = var_1624_pad_type_0, strides = var_1620, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = tensor("op_1624_cast_fp16")]; + tensor var_1625_split_sizes_0 = const()[name = tensor("op_1625_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1625_axis_0 = const()[name = tensor("op_1625_axis_0"), val = tensor(1)]; + tensor var_1625_cast_fp16_0, tensor var_1625_cast_fp16_1 = split(axis = var_1625_axis_0, split_sizes = var_1625_split_sizes_0, x = var_1624_cast_fp16)[name = tensor("op_1625_cast_fp16")]; + tensor var_1627_mode_0 = const()[name = tensor("op_1627_mode_0"), val = tensor("EXACT")]; + tensor var_1627_cast_fp16 = gelu(mode = var_1627_mode_0, x = var_1625_cast_fp16_1)[name = tensor("op_1627_cast_fp16")]; + tensor input_145_cast_fp16 = mul(x = var_1625_cast_fp16_0, y = var_1627_cast_fp16)[name = tensor("input_145_cast_fp16")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; + tensor var_1635_pad_type_0 = const()[name = tensor("op_1635_pad_type_0"), val = tensor("custom")]; + tensor var_1635_pad_0 = const()[name = tensor("op_1635_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121666368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126581632))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126581824)))]; + tensor var_1635_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1633, groups = var_1186, pad = var_1635_pad_0, pad_type = var_1635_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_145_cast_fp16)[name = tensor("op_1635_cast_fp16")]; + tensor inputs_37_cast_fp16 = add(x = var_1635_cast_fp16, y = inputs_35_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; + tensor hidden_states_77_axes_0 = const()[name = tensor("hidden_states_77_axes_0"), val = tensor([1])]; + tensor hidden_states_77_gamma_0_to_fp16 = const()[name = tensor("hidden_states_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126584448)))]; + tensor hidden_states_77_beta_0_to_fp16 = const()[name = tensor("hidden_states_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126587072)))]; + tensor var_1651_to_fp16 = const()[name = tensor("op_1651_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_77_cast_fp16 = layer_norm(axes = hidden_states_77_axes_0, beta = hidden_states_77_beta_0_to_fp16, epsilon = var_1651_to_fp16, gamma = hidden_states_77_gamma_0_to_fp16, x = inputs_37_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor var_1666 = const()[name = tensor("op_1666"), val = tensor([1, 1])]; + tensor var_1668 = const()[name = tensor("op_1668"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126589696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127818560))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_25_cast_fp16 = conv(dilations = var_1668, groups = var_1186, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1666, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_77_cast_fp16)[name = tensor("q_25_cast_fp16")]; + tensor var_1672 = const()[name = tensor("op_1672"), val = tensor([1, 1])]; + tensor var_1674 = const()[name = tensor("op_1674"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127818752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129047616))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_25_cast_fp16 = conv(dilations = var_1674, groups = var_1186, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1672, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_77_cast_fp16)[name = tensor("k_25_cast_fp16")]; + tensor var_1678 = const()[name = tensor("op_1678"), val = tensor([1, 1])]; + tensor var_1680 = const()[name = tensor("op_1680"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129047808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130276672))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_25_cast_fp16 = conv(dilations = var_1680, groups = var_1186, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1678, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_77_cast_fp16)[name = tensor("v_25_cast_fp16")]; + tensor var_1684 = const()[name = tensor("op_1684"), val = tensor([1, 20, 64, -1])]; + tensor var_1685_cast_fp16 = reshape(shape = var_1684, x = q_25_cast_fp16)[name = tensor("op_1685_cast_fp16")]; + tensor var_1686 = const()[name = tensor("op_1686"), val = tensor([1, 20, 64, -1])]; + tensor var_1687_cast_fp16 = reshape(shape = var_1686, x = k_25_cast_fp16)[name = tensor("op_1687_cast_fp16")]; + tensor var_1688 = const()[name = tensor("op_1688"), val = tensor([1, 20, 64, -1])]; + tensor var_1689_cast_fp16 = reshape(shape = var_1688, x = v_25_cast_fp16)[name = tensor("op_1689_cast_fp16")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1685_cast_fp16, y = var_1687_cast_fp16)[name = tensor("attn_weights_49_cast_fp16")]; + tensor attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_51_cast_fp16")]; + tensor var_1693_cast_fp16 = softmax(axis = var_1170, x = attn_weights_51_cast_fp16)[name = tensor("op_1693_cast_fp16")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1689_cast_fp16, y = var_1693_cast_fp16)[name = tensor("attn_25_cast_fp16")]; + tensor var_1697 = const()[name = tensor("op_1697"), val = tensor([1, 1280, 1, -1])]; + tensor input_147_cast_fp16 = reshape(shape = var_1697, x = attn_25_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([1, 1])]; + tensor var_1704 = const()[name = tensor("op_1704"), val = tensor([1, 1])]; + tensor var_1706_pad_type_0 = const()[name = tensor("op_1706_pad_type_0"), val = tensor("custom")]; + tensor var_1706_pad_0 = const()[name = tensor("op_1706_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130276864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131505728))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131505920)))]; + tensor var_1706_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_1704, groups = var_1186, pad = var_1706_pad_0, pad_type = var_1706_pad_type_0, strides = var_1702, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor("op_1706_cast_fp16")]; + tensor inputs_39_cast_fp16 = add(x = var_1706_cast_fp16, y = inputs_37_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; + tensor hidden_states_79_axes_0 = const()[name = tensor("hidden_states_79_axes_0"), val = tensor([1])]; + tensor hidden_states_79_gamma_0_to_fp16 = const()[name = tensor("hidden_states_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131508544)))]; + tensor hidden_states_79_beta_0_to_fp16 = const()[name = tensor("hidden_states_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131511168)))]; + tensor var_1716_to_fp16 = const()[name = tensor("op_1716_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_79_cast_fp16 = layer_norm(axes = hidden_states_79_axes_0, beta = hidden_states_79_beta_0_to_fp16, epsilon = var_1716_to_fp16, gamma = hidden_states_79_gamma_0_to_fp16, x = inputs_39_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor var_1731 = const()[name = tensor("op_1731"), val = tensor([1, 1])]; + tensor var_1733 = const()[name = tensor("op_1733"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131513792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132742656))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_27_cast_fp16 = conv(dilations = var_1733, groups = var_1186, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1731, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_79_cast_fp16)[name = tensor("q_27_cast_fp16")]; + tensor var_1737 = const()[name = tensor("op_1737"), val = tensor([1, 1])]; + tensor var_1739 = const()[name = tensor("op_1739"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132742848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134708992))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_27_cast_fp16 = conv(dilations = var_1739, groups = var_1186, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1737, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_27_cast_fp16")]; + tensor var_1743 = const()[name = tensor("op_1743"), val = tensor([1, 1])]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134709184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136675328))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_27_cast_fp16 = conv(dilations = var_1745, groups = var_1186, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1743, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_27_cast_fp16")]; + tensor var_1749 = const()[name = tensor("op_1749"), val = tensor([1, 20, 64, -1])]; + tensor var_1750_cast_fp16 = reshape(shape = var_1749, x = q_27_cast_fp16)[name = tensor("op_1750_cast_fp16")]; + tensor var_1751 = const()[name = tensor("op_1751"), val = tensor([1, 20, 64, -1])]; + tensor var_1752_cast_fp16 = reshape(shape = var_1751, x = k_27_cast_fp16)[name = tensor("op_1752_cast_fp16")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 20, 64, -1])]; + tensor var_1754_cast_fp16 = reshape(shape = var_1753, x = v_27_cast_fp16)[name = tensor("op_1754_cast_fp16")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast_fp16 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1750_cast_fp16, y = var_1752_cast_fp16)[name = tensor("attn_weights_53_cast_fp16")]; + tensor attn_weights_55_cast_fp16 = mul(x = attn_weights_53_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_55_cast_fp16")]; + tensor var_1758_cast_fp16 = softmax(axis = var_1170, x = attn_weights_55_cast_fp16)[name = tensor("op_1758_cast_fp16")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1754_cast_fp16, y = var_1758_cast_fp16)[name = tensor("attn_27_cast_fp16")]; + tensor var_1762 = const()[name = tensor("op_1762"), val = tensor([1, 1280, 1, -1])]; + tensor input_149_cast_fp16 = reshape(shape = var_1762, x = attn_27_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; + tensor var_1769 = const()[name = tensor("op_1769"), val = tensor([1, 1])]; + tensor var_1771_pad_type_0 = const()[name = tensor("op_1771_pad_type_0"), val = tensor("custom")]; + tensor var_1771_pad_0 = const()[name = tensor("op_1771_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136675520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137904384))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137904576)))]; + tensor var_1771_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_1769, groups = var_1186, pad = var_1771_pad_0, pad_type = var_1771_pad_type_0, strides = var_1767, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_149_cast_fp16)[name = tensor("op_1771_cast_fp16")]; + tensor inputs_41_cast_fp16 = add(x = var_1771_cast_fp16, y = inputs_39_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; + tensor input_151_axes_0 = const()[name = tensor("input_151_axes_0"), val = tensor([1])]; + tensor input_151_gamma_0_to_fp16 = const()[name = tensor("input_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137907200)))]; + tensor input_151_beta_0_to_fp16 = const()[name = tensor("input_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137909824)))]; + tensor var_1781_to_fp16 = const()[name = tensor("op_1781_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_151_cast_fp16 = layer_norm(axes = input_151_axes_0, beta = input_151_beta_0_to_fp16, epsilon = var_1781_to_fp16, gamma = input_151_gamma_0_to_fp16, x = inputs_41_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor var_1797 = const()[name = tensor("op_1797"), val = tensor([1, 1])]; + tensor var_1799 = const()[name = tensor("op_1799"), val = tensor([1, 1])]; + tensor var_1801_pad_type_0 = const()[name = tensor("op_1801_pad_type_0"), val = tensor("custom")]; + tensor var_1801_pad_0 = const()[name = tensor("op_1801_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137912448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147742912))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147743104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147750848))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1801_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1799, groups = var_1186, pad = var_1801_pad_0, pad_type = var_1801_pad_type_0, strides = var_1797, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor("op_1801_cast_fp16")]; + tensor var_1802_split_sizes_0 = const()[name = tensor("op_1802_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1802_axis_0 = const()[name = tensor("op_1802_axis_0"), val = tensor(1)]; + tensor var_1802_cast_fp16_0, tensor var_1802_cast_fp16_1 = split(axis = var_1802_axis_0, split_sizes = var_1802_split_sizes_0, x = var_1801_cast_fp16)[name = tensor("op_1802_cast_fp16")]; + tensor var_1804_mode_0 = const()[name = tensor("op_1804_mode_0"), val = tensor("EXACT")]; + tensor var_1804_cast_fp16 = gelu(mode = var_1804_mode_0, x = var_1802_cast_fp16_1)[name = tensor("op_1804_cast_fp16")]; + tensor input_153_cast_fp16 = mul(x = var_1802_cast_fp16_0, y = var_1804_cast_fp16)[name = tensor("input_153_cast_fp16")]; + tensor var_1808 = const()[name = tensor("op_1808"), val = tensor([1, 1])]; + tensor var_1810 = const()[name = tensor("op_1810"), val = tensor([1, 1])]; + tensor var_1812_pad_type_0 = const()[name = tensor("op_1812_pad_type_0"), val = tensor("custom")]; + tensor var_1812_pad_0 = const()[name = tensor("op_1812_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147751040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152666304))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152666496)))]; + tensor var_1812_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_1810, groups = var_1186, pad = var_1812_pad_0, pad_type = var_1812_pad_type_0, strides = var_1808, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = tensor("op_1812_cast_fp16")]; + tensor inputs_43_cast_fp16 = add(x = var_1812_cast_fp16, y = inputs_41_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor hidden_states_83_axes_0 = const()[name = tensor("hidden_states_83_axes_0"), val = tensor([1])]; + tensor hidden_states_83_gamma_0_to_fp16 = const()[name = tensor("hidden_states_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152669120)))]; + tensor hidden_states_83_beta_0_to_fp16 = const()[name = tensor("hidden_states_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152671744)))]; + tensor var_1828_to_fp16 = const()[name = tensor("op_1828_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_83_cast_fp16 = layer_norm(axes = hidden_states_83_axes_0, beta = hidden_states_83_beta_0_to_fp16, epsilon = var_1828_to_fp16, gamma = hidden_states_83_gamma_0_to_fp16, x = inputs_43_cast_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_1843 = const()[name = tensor("op_1843"), val = tensor([1, 1])]; + tensor var_1845 = const()[name = tensor("op_1845"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152674368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153903232))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_29_cast_fp16 = conv(dilations = var_1845, groups = var_1186, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1843, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("q_29_cast_fp16")]; + tensor var_1849 = const()[name = tensor("op_1849"), val = tensor([1, 1])]; + tensor var_1851 = const()[name = tensor("op_1851"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153903424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155132288))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_29_cast_fp16 = conv(dilations = var_1851, groups = var_1186, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1849, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("k_29_cast_fp16")]; + tensor var_1855 = const()[name = tensor("op_1855"), val = tensor([1, 1])]; + tensor var_1857 = const()[name = tensor("op_1857"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155132480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156361344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_29_cast_fp16 = conv(dilations = var_1857, groups = var_1186, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1855, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_83_cast_fp16)[name = tensor("v_29_cast_fp16")]; + tensor var_1861 = const()[name = tensor("op_1861"), val = tensor([1, 20, 64, -1])]; + tensor var_1862_cast_fp16 = reshape(shape = var_1861, x = q_29_cast_fp16)[name = tensor("op_1862_cast_fp16")]; + tensor var_1863 = const()[name = tensor("op_1863"), val = tensor([1, 20, 64, -1])]; + tensor var_1864_cast_fp16 = reshape(shape = var_1863, x = k_29_cast_fp16)[name = tensor("op_1864_cast_fp16")]; + tensor var_1865 = const()[name = tensor("op_1865"), val = tensor([1, 20, 64, -1])]; + tensor var_1866_cast_fp16 = reshape(shape = var_1865, x = v_29_cast_fp16)[name = tensor("op_1866_cast_fp16")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1862_cast_fp16, y = var_1864_cast_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_59_cast_fp16")]; + tensor var_1870_cast_fp16 = softmax(axis = var_1170, x = attn_weights_59_cast_fp16)[name = tensor("op_1870_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1866_cast_fp16, y = var_1870_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_1874 = const()[name = tensor("op_1874"), val = tensor([1, 1280, 1, -1])]; + tensor input_155_cast_fp16 = reshape(shape = var_1874, x = attn_29_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor var_1879 = const()[name = tensor("op_1879"), val = tensor([1, 1])]; + tensor var_1881 = const()[name = tensor("op_1881"), val = tensor([1, 1])]; + tensor var_1883_pad_type_0 = const()[name = tensor("op_1883_pad_type_0"), val = tensor("custom")]; + tensor var_1883_pad_0 = const()[name = tensor("op_1883_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156361536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157590400))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157590592)))]; + tensor var_1883_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_1881, groups = var_1186, pad = var_1883_pad_0, pad_type = var_1883_pad_type_0, strides = var_1879, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor("op_1883_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = var_1883_cast_fp16, y = inputs_43_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor hidden_states_85_axes_0 = const()[name = tensor("hidden_states_85_axes_0"), val = tensor([1])]; + tensor hidden_states_85_gamma_0_to_fp16 = const()[name = tensor("hidden_states_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157593216)))]; + tensor hidden_states_85_beta_0_to_fp16 = const()[name = tensor("hidden_states_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157595840)))]; + tensor var_1893_to_fp16 = const()[name = tensor("op_1893_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_85_cast_fp16 = layer_norm(axes = hidden_states_85_axes_0, beta = hidden_states_85_beta_0_to_fp16, epsilon = var_1893_to_fp16, gamma = hidden_states_85_gamma_0_to_fp16, x = inputs_45_cast_fp16)[name = tensor("hidden_states_85_cast_fp16")]; + tensor var_1908 = const()[name = tensor("op_1908"), val = tensor([1, 1])]; + tensor var_1910 = const()[name = tensor("op_1910"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(157598464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158827328))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_31_cast_fp16 = conv(dilations = var_1910, groups = var_1186, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1908, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_85_cast_fp16)[name = tensor("q_31_cast_fp16")]; + tensor var_1914 = const()[name = tensor("op_1914"), val = tensor([1, 1])]; + tensor var_1916 = const()[name = tensor("op_1916"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158827520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160793664))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_31_cast_fp16 = conv(dilations = var_1916, groups = var_1186, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1914, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_31_cast_fp16")]; + tensor var_1920 = const()[name = tensor("op_1920"), val = tensor([1, 1])]; + tensor var_1922 = const()[name = tensor("op_1922"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160793856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162760000))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_31_cast_fp16 = conv(dilations = var_1922, groups = var_1186, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1920, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_31_cast_fp16")]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 20, 64, -1])]; + tensor var_1927_cast_fp16 = reshape(shape = var_1926, x = q_31_cast_fp16)[name = tensor("op_1927_cast_fp16")]; + tensor var_1928 = const()[name = tensor("op_1928"), val = tensor([1, 20, 64, -1])]; + tensor var_1929_cast_fp16 = reshape(shape = var_1928, x = k_31_cast_fp16)[name = tensor("op_1929_cast_fp16")]; + tensor var_1930 = const()[name = tensor("op_1930"), val = tensor([1, 20, 64, -1])]; + tensor var_1931_cast_fp16 = reshape(shape = var_1930, x = v_31_cast_fp16)[name = tensor("op_1931_cast_fp16")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1927_cast_fp16, y = var_1929_cast_fp16)[name = tensor("attn_weights_61_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = mul(x = attn_weights_61_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_1935_cast_fp16 = softmax(axis = var_1170, x = attn_weights_63_cast_fp16)[name = tensor("op_1935_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1931_cast_fp16, y = var_1935_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_1939 = const()[name = tensor("op_1939"), val = tensor([1, 1280, 1, -1])]; + tensor input_157_cast_fp16 = reshape(shape = var_1939, x = attn_31_cast_fp16)[name = tensor("input_157_cast_fp16")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([1, 1])]; + tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([1, 1])]; + tensor var_1948_pad_type_0 = const()[name = tensor("op_1948_pad_type_0"), val = tensor("custom")]; + tensor var_1948_pad_0 = const()[name = tensor("op_1948_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162760192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163989056))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163989248)))]; + tensor var_1948_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1946, groups = var_1186, pad = var_1948_pad_0, pad_type = var_1948_pad_type_0, strides = var_1944, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = tensor("op_1948_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = var_1948_cast_fp16, y = inputs_45_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor input_159_axes_0 = const()[name = tensor("input_159_axes_0"), val = tensor([1])]; + tensor input_159_gamma_0_to_fp16 = const()[name = tensor("input_159_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163991872)))]; + tensor input_159_beta_0_to_fp16 = const()[name = tensor("input_159_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163994496)))]; + tensor var_1958_to_fp16 = const()[name = tensor("op_1958_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_159_cast_fp16 = layer_norm(axes = input_159_axes_0, beta = input_159_beta_0_to_fp16, epsilon = var_1958_to_fp16, gamma = input_159_gamma_0_to_fp16, x = inputs_47_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1, 1])]; + tensor var_1976 = const()[name = tensor("op_1976"), val = tensor([1, 1])]; + tensor var_1978_pad_type_0 = const()[name = tensor("op_1978_pad_type_0"), val = tensor("custom")]; + tensor var_1978_pad_0 = const()[name = tensor("op_1978_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163997120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173827584))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173827776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173835520))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1978_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1976, groups = var_1186, pad = var_1978_pad_0, pad_type = var_1978_pad_type_0, strides = var_1974, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_159_cast_fp16)[name = tensor("op_1978_cast_fp16")]; + tensor var_1979_split_sizes_0 = const()[name = tensor("op_1979_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1979_axis_0 = const()[name = tensor("op_1979_axis_0"), val = tensor(1)]; + tensor var_1979_cast_fp16_0, tensor var_1979_cast_fp16_1 = split(axis = var_1979_axis_0, split_sizes = var_1979_split_sizes_0, x = var_1978_cast_fp16)[name = tensor("op_1979_cast_fp16")]; + tensor var_1981_mode_0 = const()[name = tensor("op_1981_mode_0"), val = tensor("EXACT")]; + tensor var_1981_cast_fp16 = gelu(mode = var_1981_mode_0, x = var_1979_cast_fp16_1)[name = tensor("op_1981_cast_fp16")]; + tensor input_161_cast_fp16 = mul(x = var_1979_cast_fp16_0, y = var_1981_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor var_1985 = const()[name = tensor("op_1985"), val = tensor([1, 1])]; + tensor var_1987 = const()[name = tensor("op_1987"), val = tensor([1, 1])]; + tensor var_1989_pad_type_0 = const()[name = tensor("op_1989_pad_type_0"), val = tensor("custom")]; + tensor var_1989_pad_0 = const()[name = tensor("op_1989_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173835712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178750976))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178751168)))]; + tensor var_1989_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1987, groups = var_1186, pad = var_1989_pad_0, pad_type = var_1989_pad_type_0, strides = var_1985, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = tensor("op_1989_cast_fp16")]; + tensor inputs_49_cast_fp16 = add(x = var_1989_cast_fp16, y = inputs_47_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor hidden_states_89_axes_0 = const()[name = tensor("hidden_states_89_axes_0"), val = tensor([1])]; + tensor hidden_states_89_gamma_0_to_fp16 = const()[name = tensor("hidden_states_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178753792)))]; + tensor hidden_states_89_beta_0_to_fp16 = const()[name = tensor("hidden_states_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178756416)))]; + tensor var_2005_to_fp16 = const()[name = tensor("op_2005_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_89_cast_fp16 = layer_norm(axes = hidden_states_89_axes_0, beta = hidden_states_89_beta_0_to_fp16, epsilon = var_2005_to_fp16, gamma = hidden_states_89_gamma_0_to_fp16, x = inputs_49_cast_fp16)[name = tensor("hidden_states_89_cast_fp16")]; + tensor var_2020 = const()[name = tensor("op_2020"), val = tensor([1, 1])]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178759040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179987904))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_33_cast_fp16 = conv(dilations = var_2022, groups = var_1186, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2020, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = tensor("q_33_cast_fp16")]; + tensor var_2026 = const()[name = tensor("op_2026"), val = tensor([1, 1])]; + tensor var_2028 = const()[name = tensor("op_2028"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179988096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181216960))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_33_cast_fp16 = conv(dilations = var_2028, groups = var_1186, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2026, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = tensor("k_33_cast_fp16")]; + tensor var_2032 = const()[name = tensor("op_2032"), val = tensor([1, 1])]; + tensor var_2034 = const()[name = tensor("op_2034"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181217152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182446016))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_33_cast_fp16 = conv(dilations = var_2034, groups = var_1186, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2032, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_89_cast_fp16)[name = tensor("v_33_cast_fp16")]; + tensor var_2038 = const()[name = tensor("op_2038"), val = tensor([1, 20, 64, -1])]; + tensor var_2039_cast_fp16 = reshape(shape = var_2038, x = q_33_cast_fp16)[name = tensor("op_2039_cast_fp16")]; + tensor var_2040 = const()[name = tensor("op_2040"), val = tensor([1, 20, 64, -1])]; + tensor var_2041_cast_fp16 = reshape(shape = var_2040, x = k_33_cast_fp16)[name = tensor("op_2041_cast_fp16")]; + tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([1, 20, 64, -1])]; + tensor var_2043_cast_fp16 = reshape(shape = var_2042, x = v_33_cast_fp16)[name = tensor("op_2043_cast_fp16")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2039_cast_fp16, y = var_2041_cast_fp16)[name = tensor("attn_weights_65_cast_fp16")]; + tensor attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_2047_cast_fp16 = softmax(axis = var_1170, x = attn_weights_67_cast_fp16)[name = tensor("op_2047_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2043_cast_fp16, y = var_2047_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_2051 = const()[name = tensor("op_2051"), val = tensor([1, 1280, 1, -1])]; + tensor input_163_cast_fp16 = reshape(shape = var_2051, x = attn_33_cast_fp16)[name = tensor("input_163_cast_fp16")]; + tensor var_2056 = const()[name = tensor("op_2056"), val = tensor([1, 1])]; + tensor var_2058 = const()[name = tensor("op_2058"), val = tensor([1, 1])]; + tensor var_2060_pad_type_0 = const()[name = tensor("op_2060_pad_type_0"), val = tensor("custom")]; + tensor var_2060_pad_0 = const()[name = tensor("op_2060_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182446208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183675072))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183675264)))]; + tensor var_2060_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_2058, groups = var_1186, pad = var_2060_pad_0, pad_type = var_2060_pad_type_0, strides = var_2056, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = tensor("op_2060_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = var_2060_cast_fp16, y = inputs_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor hidden_states_91_axes_0 = const()[name = tensor("hidden_states_91_axes_0"), val = tensor([1])]; + tensor hidden_states_91_gamma_0_to_fp16 = const()[name = tensor("hidden_states_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183677888)))]; + tensor hidden_states_91_beta_0_to_fp16 = const()[name = tensor("hidden_states_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183680512)))]; + tensor var_2070_to_fp16 = const()[name = tensor("op_2070_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_91_cast_fp16 = layer_norm(axes = hidden_states_91_axes_0, beta = hidden_states_91_beta_0_to_fp16, epsilon = var_2070_to_fp16, gamma = hidden_states_91_gamma_0_to_fp16, x = inputs_51_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_2085 = const()[name = tensor("op_2085"), val = tensor([1, 1])]; + tensor var_2087 = const()[name = tensor("op_2087"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183683136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184912000))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_35_cast_fp16 = conv(dilations = var_2087, groups = var_1186, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2085, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_91_cast_fp16)[name = tensor("q_35_cast_fp16")]; + tensor var_2091 = const()[name = tensor("op_2091"), val = tensor([1, 1])]; + tensor var_2093 = const()[name = tensor("op_2093"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184912192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186878336))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_35_cast_fp16 = conv(dilations = var_2093, groups = var_1186, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2091, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_35_cast_fp16")]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; + tensor var_2099 = const()[name = tensor("op_2099"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186878528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188844672))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_35_cast_fp16 = conv(dilations = var_2099, groups = var_1186, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2097, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_35_cast_fp16")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 20, 64, -1])]; + tensor var_2104_cast_fp16 = reshape(shape = var_2103, x = q_35_cast_fp16)[name = tensor("op_2104_cast_fp16")]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([1, 20, 64, -1])]; + tensor var_2106_cast_fp16 = reshape(shape = var_2105, x = k_35_cast_fp16)[name = tensor("op_2106_cast_fp16")]; + tensor var_2107 = const()[name = tensor("op_2107"), val = tensor([1, 20, 64, -1])]; + tensor var_2108_cast_fp16 = reshape(shape = var_2107, x = v_35_cast_fp16)[name = tensor("op_2108_cast_fp16")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast_fp16 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2104_cast_fp16, y = var_2106_cast_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor attn_weights_71_cast_fp16 = mul(x = attn_weights_69_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_71_cast_fp16")]; + tensor var_2112_cast_fp16 = softmax(axis = var_1170, x = attn_weights_71_cast_fp16)[name = tensor("op_2112_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2108_cast_fp16, y = var_2112_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_2116 = const()[name = tensor("op_2116"), val = tensor([1, 1280, 1, -1])]; + tensor input_165_cast_fp16 = reshape(shape = var_2116, x = attn_35_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor var_2121 = const()[name = tensor("op_2121"), val = tensor([1, 1])]; + tensor var_2123 = const()[name = tensor("op_2123"), val = tensor([1, 1])]; + tensor var_2125_pad_type_0 = const()[name = tensor("op_2125_pad_type_0"), val = tensor("custom")]; + tensor var_2125_pad_0 = const()[name = tensor("op_2125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188844864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190073728))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190073920)))]; + tensor var_2125_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_2123, groups = var_1186, pad = var_2125_pad_0, pad_type = var_2125_pad_type_0, strides = var_2121, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = tensor("op_2125_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = var_2125_cast_fp16, y = inputs_51_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor input_167_axes_0 = const()[name = tensor("input_167_axes_0"), val = tensor([1])]; + tensor input_167_gamma_0_to_fp16 = const()[name = tensor("input_167_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190076544)))]; + tensor input_167_beta_0_to_fp16 = const()[name = tensor("input_167_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190079168)))]; + tensor var_2135_to_fp16 = const()[name = tensor("op_2135_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_167_cast_fp16 = layer_norm(axes = input_167_axes_0, beta = input_167_beta_0_to_fp16, epsilon = var_2135_to_fp16, gamma = input_167_gamma_0_to_fp16, x = inputs_53_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor var_2155_pad_type_0 = const()[name = tensor("op_2155_pad_type_0"), val = tensor("custom")]; + tensor var_2155_pad_0 = const()[name = tensor("op_2155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190081792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199912256))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199912448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199920192))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2155_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2153, groups = var_1186, pad = var_2155_pad_0, pad_type = var_2155_pad_type_0, strides = var_2151, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("op_2155_cast_fp16")]; + tensor var_2156_split_sizes_0 = const()[name = tensor("op_2156_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2156_axis_0 = const()[name = tensor("op_2156_axis_0"), val = tensor(1)]; + tensor var_2156_cast_fp16_0, tensor var_2156_cast_fp16_1 = split(axis = var_2156_axis_0, split_sizes = var_2156_split_sizes_0, x = var_2155_cast_fp16)[name = tensor("op_2156_cast_fp16")]; + tensor var_2158_mode_0 = const()[name = tensor("op_2158_mode_0"), val = tensor("EXACT")]; + tensor var_2158_cast_fp16 = gelu(mode = var_2158_mode_0, x = var_2156_cast_fp16_1)[name = tensor("op_2158_cast_fp16")]; + tensor input_169_cast_fp16 = mul(x = var_2156_cast_fp16_0, y = var_2158_cast_fp16)[name = tensor("input_169_cast_fp16")]; + tensor var_2162 = const()[name = tensor("op_2162"), val = tensor([1, 1])]; + tensor var_2164 = const()[name = tensor("op_2164"), val = tensor([1, 1])]; + tensor var_2166_pad_type_0 = const()[name = tensor("op_2166_pad_type_0"), val = tensor("custom")]; + tensor var_2166_pad_0 = const()[name = tensor("op_2166_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199920384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204835648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204835840)))]; + tensor var_2166_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_2164, groups = var_1186, pad = var_2166_pad_0, pad_type = var_2166_pad_type_0, strides = var_2162, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_169_cast_fp16)[name = tensor("op_2166_cast_fp16")]; + tensor inputs_55_cast_fp16 = add(x = var_2166_cast_fp16, y = inputs_53_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor hidden_states_95_axes_0 = const()[name = tensor("hidden_states_95_axes_0"), val = tensor([1])]; + tensor hidden_states_95_gamma_0_to_fp16 = const()[name = tensor("hidden_states_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204838464)))]; + tensor hidden_states_95_beta_0_to_fp16 = const()[name = tensor("hidden_states_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204841088)))]; + tensor var_2182_to_fp16 = const()[name = tensor("op_2182_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_95_cast_fp16 = layer_norm(axes = hidden_states_95_axes_0, beta = hidden_states_95_beta_0_to_fp16, epsilon = var_2182_to_fp16, gamma = hidden_states_95_gamma_0_to_fp16, x = inputs_55_cast_fp16)[name = tensor("hidden_states_95_cast_fp16")]; + tensor var_2197 = const()[name = tensor("op_2197"), val = tensor([1, 1])]; + tensor var_2199 = const()[name = tensor("op_2199"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204843712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206072576))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_37_cast_fp16 = conv(dilations = var_2199, groups = var_1186, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2197, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_95_cast_fp16)[name = tensor("q_37_cast_fp16")]; + tensor var_2203 = const()[name = tensor("op_2203"), val = tensor([1, 1])]; + tensor var_2205 = const()[name = tensor("op_2205"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206072768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207301632))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_37_cast_fp16 = conv(dilations = var_2205, groups = var_1186, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2203, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_95_cast_fp16)[name = tensor("k_37_cast_fp16")]; + tensor var_2209 = const()[name = tensor("op_2209"), val = tensor([1, 1])]; + tensor var_2211 = const()[name = tensor("op_2211"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207301824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208530688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_37_cast_fp16 = conv(dilations = var_2211, groups = var_1186, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2209, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_95_cast_fp16)[name = tensor("v_37_cast_fp16")]; + tensor var_2215 = const()[name = tensor("op_2215"), val = tensor([1, 20, 64, -1])]; + tensor var_2216_cast_fp16 = reshape(shape = var_2215, x = q_37_cast_fp16)[name = tensor("op_2216_cast_fp16")]; + tensor var_2217 = const()[name = tensor("op_2217"), val = tensor([1, 20, 64, -1])]; + tensor var_2218_cast_fp16 = reshape(shape = var_2217, x = k_37_cast_fp16)[name = tensor("op_2218_cast_fp16")]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([1, 20, 64, -1])]; + tensor var_2220_cast_fp16 = reshape(shape = var_2219, x = v_37_cast_fp16)[name = tensor("op_2220_cast_fp16")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2216_cast_fp16, y = var_2218_cast_fp16)[name = tensor("attn_weights_73_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_2224_cast_fp16 = softmax(axis = var_1170, x = attn_weights_75_cast_fp16)[name = tensor("op_2224_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2220_cast_fp16, y = var_2224_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_2228 = const()[name = tensor("op_2228"), val = tensor([1, 1280, 1, -1])]; + tensor input_171_cast_fp16 = reshape(shape = var_2228, x = attn_37_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor var_2233 = const()[name = tensor("op_2233"), val = tensor([1, 1])]; + tensor var_2235 = const()[name = tensor("op_2235"), val = tensor([1, 1])]; + tensor var_2237_pad_type_0 = const()[name = tensor("op_2237_pad_type_0"), val = tensor("custom")]; + tensor var_2237_pad_0 = const()[name = tensor("op_2237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208530880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209759744))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209759936)))]; + tensor var_2237_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_2235, groups = var_1186, pad = var_2237_pad_0, pad_type = var_2237_pad_type_0, strides = var_2233, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = tensor("op_2237_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = var_2237_cast_fp16, y = inputs_55_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor hidden_states_97_axes_0 = const()[name = tensor("hidden_states_97_axes_0"), val = tensor([1])]; + tensor hidden_states_97_gamma_0_to_fp16 = const()[name = tensor("hidden_states_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209762560)))]; + tensor hidden_states_97_beta_0_to_fp16 = const()[name = tensor("hidden_states_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209765184)))]; + tensor var_2247_to_fp16 = const()[name = tensor("op_2247_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_97_cast_fp16 = layer_norm(axes = hidden_states_97_axes_0, beta = hidden_states_97_beta_0_to_fp16, epsilon = var_2247_to_fp16, gamma = hidden_states_97_gamma_0_to_fp16, x = inputs_57_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, 1])]; + tensor var_2264 = const()[name = tensor("op_2264"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209767808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210996672))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_39_cast_fp16 = conv(dilations = var_2264, groups = var_1186, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2262, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_97_cast_fp16)[name = tensor("q_39_cast_fp16")]; + tensor var_2268 = const()[name = tensor("op_2268"), val = tensor([1, 1])]; + tensor var_2270 = const()[name = tensor("op_2270"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210996864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212963008))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_39_cast_fp16 = conv(dilations = var_2270, groups = var_1186, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2268, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_39_cast_fp16")]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor var_2276 = const()[name = tensor("op_2276"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212963200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214929344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_39_cast_fp16 = conv(dilations = var_2276, groups = var_1186, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2274, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_39_cast_fp16")]; + tensor var_2280 = const()[name = tensor("op_2280"), val = tensor([1, 20, 64, -1])]; + tensor var_2281_cast_fp16 = reshape(shape = var_2280, x = q_39_cast_fp16)[name = tensor("op_2281_cast_fp16")]; + tensor var_2282 = const()[name = tensor("op_2282"), val = tensor([1, 20, 64, -1])]; + tensor var_2283_cast_fp16 = reshape(shape = var_2282, x = k_39_cast_fp16)[name = tensor("op_2283_cast_fp16")]; + tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, 20, 64, -1])]; + tensor var_2285_cast_fp16 = reshape(shape = var_2284, x = v_39_cast_fp16)[name = tensor("op_2285_cast_fp16")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast_fp16 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2281_cast_fp16, y = var_2283_cast_fp16)[name = tensor("attn_weights_77_cast_fp16")]; + tensor attn_weights_79_cast_fp16 = mul(x = attn_weights_77_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_2289_cast_fp16 = softmax(axis = var_1170, x = attn_weights_79_cast_fp16)[name = tensor("op_2289_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2285_cast_fp16, y = var_2289_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_2293 = const()[name = tensor("op_2293"), val = tensor([1, 1280, 1, -1])]; + tensor input_173_cast_fp16 = reshape(shape = var_2293, x = attn_39_cast_fp16)[name = tensor("input_173_cast_fp16")]; + tensor var_2298 = const()[name = tensor("op_2298"), val = tensor([1, 1])]; + tensor var_2300 = const()[name = tensor("op_2300"), val = tensor([1, 1])]; + tensor var_2302_pad_type_0 = const()[name = tensor("op_2302_pad_type_0"), val = tensor("custom")]; + tensor var_2302_pad_0 = const()[name = tensor("op_2302_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214929536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216158400))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216158592)))]; + tensor var_2302_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_2300, groups = var_1186, pad = var_2302_pad_0, pad_type = var_2302_pad_type_0, strides = var_2298, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = tensor("op_2302_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = var_2302_cast_fp16, y = inputs_57_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor input_175_axes_0 = const()[name = tensor("input_175_axes_0"), val = tensor([1])]; + tensor input_175_gamma_0_to_fp16 = const()[name = tensor("input_175_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216161216)))]; + tensor input_175_beta_0_to_fp16 = const()[name = tensor("input_175_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216163840)))]; + tensor var_2312_to_fp16 = const()[name = tensor("op_2312_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = input_175_beta_0_to_fp16, epsilon = var_2312_to_fp16, gamma = input_175_gamma_0_to_fp16, x = inputs_59_cast_fp16)[name = tensor("input_175_cast_fp16")]; + tensor var_2328 = const()[name = tensor("op_2328"), val = tensor([1, 1])]; + tensor var_2330 = const()[name = tensor("op_2330"), val = tensor([1, 1])]; + tensor var_2332_pad_type_0 = const()[name = tensor("op_2332_pad_type_0"), val = tensor("custom")]; + tensor var_2332_pad_0 = const()[name = tensor("op_2332_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216166464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(225996928))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(225997120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226004864))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2332_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2330, groups = var_1186, pad = var_2332_pad_0, pad_type = var_2332_pad_type_0, strides = var_2328, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = tensor("op_2332_cast_fp16")]; + tensor var_2333_split_sizes_0 = const()[name = tensor("op_2333_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2333_axis_0 = const()[name = tensor("op_2333_axis_0"), val = tensor(1)]; + tensor var_2333_cast_fp16_0, tensor var_2333_cast_fp16_1 = split(axis = var_2333_axis_0, split_sizes = var_2333_split_sizes_0, x = var_2332_cast_fp16)[name = tensor("op_2333_cast_fp16")]; + tensor var_2335_mode_0 = const()[name = tensor("op_2335_mode_0"), val = tensor("EXACT")]; + tensor var_2335_cast_fp16 = gelu(mode = var_2335_mode_0, x = var_2333_cast_fp16_1)[name = tensor("op_2335_cast_fp16")]; + tensor input_177_cast_fp16 = mul(x = var_2333_cast_fp16_0, y = var_2335_cast_fp16)[name = tensor("input_177_cast_fp16")]; + tensor var_2339 = const()[name = tensor("op_2339"), val = tensor([1, 1])]; + tensor var_2341 = const()[name = tensor("op_2341"), val = tensor([1, 1])]; + tensor var_2343_pad_type_0 = const()[name = tensor("op_2343_pad_type_0"), val = tensor("custom")]; + tensor var_2343_pad_0 = const()[name = tensor("op_2343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226005056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230920320))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230920512)))]; + tensor var_2343_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_2341, groups = var_1186, pad = var_2343_pad_0, pad_type = var_2343_pad_type_0, strides = var_2339, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor("op_2343_cast_fp16")]; + tensor inputs_61_cast_fp16 = add(x = var_2343_cast_fp16, y = inputs_59_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor hidden_states_101_axes_0 = const()[name = tensor("hidden_states_101_axes_0"), val = tensor([1])]; + tensor hidden_states_101_gamma_0_to_fp16 = const()[name = tensor("hidden_states_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230923136)))]; + tensor hidden_states_101_beta_0_to_fp16 = const()[name = tensor("hidden_states_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230925760)))]; + tensor var_2359_to_fp16 = const()[name = tensor("op_2359_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_101_cast_fp16 = layer_norm(axes = hidden_states_101_axes_0, beta = hidden_states_101_beta_0_to_fp16, epsilon = var_2359_to_fp16, gamma = hidden_states_101_gamma_0_to_fp16, x = inputs_61_cast_fp16)[name = tensor("hidden_states_101_cast_fp16")]; + tensor var_2374 = const()[name = tensor("op_2374"), val = tensor([1, 1])]; + tensor var_2376 = const()[name = tensor("op_2376"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230928384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232157248))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_41_cast_fp16 = conv(dilations = var_2376, groups = var_1186, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2374, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("q_41_cast_fp16")]; + tensor var_2380 = const()[name = tensor("op_2380"), val = tensor([1, 1])]; + tensor var_2382 = const()[name = tensor("op_2382"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232157440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233386304))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_41_cast_fp16 = conv(dilations = var_2382, groups = var_1186, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2380, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("k_41_cast_fp16")]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor var_2388 = const()[name = tensor("op_2388"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233386496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234615360))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_41_cast_fp16 = conv(dilations = var_2388, groups = var_1186, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2386, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_101_cast_fp16)[name = tensor("v_41_cast_fp16")]; + tensor var_2392 = const()[name = tensor("op_2392"), val = tensor([1, 20, 64, -1])]; + tensor var_2393_cast_fp16 = reshape(shape = var_2392, x = q_41_cast_fp16)[name = tensor("op_2393_cast_fp16")]; + tensor var_2394 = const()[name = tensor("op_2394"), val = tensor([1, 20, 64, -1])]; + tensor var_2395_cast_fp16 = reshape(shape = var_2394, x = k_41_cast_fp16)[name = tensor("op_2395_cast_fp16")]; + tensor var_2396 = const()[name = tensor("op_2396"), val = tensor([1, 20, 64, -1])]; + tensor var_2397_cast_fp16 = reshape(shape = var_2396, x = v_41_cast_fp16)[name = tensor("op_2397_cast_fp16")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2393_cast_fp16, y = var_2395_cast_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_83_cast_fp16")]; + tensor var_2401_cast_fp16 = softmax(axis = var_1170, x = attn_weights_83_cast_fp16)[name = tensor("op_2401_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2397_cast_fp16, y = var_2401_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1, 1280, 1, -1])]; + tensor input_179_cast_fp16 = reshape(shape = var_2405, x = attn_41_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor var_2410 = const()[name = tensor("op_2410"), val = tensor([1, 1])]; + tensor var_2412 = const()[name = tensor("op_2412"), val = tensor([1, 1])]; + tensor var_2414_pad_type_0 = const()[name = tensor("op_2414_pad_type_0"), val = tensor("custom")]; + tensor var_2414_pad_0 = const()[name = tensor("op_2414_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234615552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235844416))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235844608)))]; + tensor var_2414_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_2412, groups = var_1186, pad = var_2414_pad_0, pad_type = var_2414_pad_type_0, strides = var_2410, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_179_cast_fp16)[name = tensor("op_2414_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = var_2414_cast_fp16, y = inputs_61_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor hidden_states_103_axes_0 = const()[name = tensor("hidden_states_103_axes_0"), val = tensor([1])]; + tensor hidden_states_103_gamma_0_to_fp16 = const()[name = tensor("hidden_states_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235847232)))]; + tensor hidden_states_103_beta_0_to_fp16 = const()[name = tensor("hidden_states_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235849856)))]; + tensor var_2424_to_fp16 = const()[name = tensor("op_2424_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_103_cast_fp16 = layer_norm(axes = hidden_states_103_axes_0, beta = hidden_states_103_beta_0_to_fp16, epsilon = var_2424_to_fp16, gamma = hidden_states_103_gamma_0_to_fp16, x = inputs_63_cast_fp16)[name = tensor("hidden_states_103_cast_fp16")]; + tensor var_2439 = const()[name = tensor("op_2439"), val = tensor([1, 1])]; + tensor var_2441 = const()[name = tensor("op_2441"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235852480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237081344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_43_cast_fp16 = conv(dilations = var_2441, groups = var_1186, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2439, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_103_cast_fp16)[name = tensor("q_43_cast_fp16")]; + tensor var_2445 = const()[name = tensor("op_2445"), val = tensor([1, 1])]; + tensor var_2447 = const()[name = tensor("op_2447"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237081536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239047680))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_43_cast_fp16 = conv(dilations = var_2447, groups = var_1186, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2445, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_43_cast_fp16")]; + tensor var_2451 = const()[name = tensor("op_2451"), val = tensor([1, 1])]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239047872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241014016))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_43_cast_fp16 = conv(dilations = var_2453, groups = var_1186, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2451, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_43_cast_fp16")]; + tensor var_2457 = const()[name = tensor("op_2457"), val = tensor([1, 20, 64, -1])]; + tensor var_2458_cast_fp16 = reshape(shape = var_2457, x = q_43_cast_fp16)[name = tensor("op_2458_cast_fp16")]; + tensor var_2459 = const()[name = tensor("op_2459"), val = tensor([1, 20, 64, -1])]; + tensor var_2460_cast_fp16 = reshape(shape = var_2459, x = k_43_cast_fp16)[name = tensor("op_2460_cast_fp16")]; + tensor var_2461 = const()[name = tensor("op_2461"), val = tensor([1, 20, 64, -1])]; + tensor var_2462_cast_fp16 = reshape(shape = var_2461, x = v_43_cast_fp16)[name = tensor("op_2462_cast_fp16")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2458_cast_fp16, y = var_2460_cast_fp16)[name = tensor("attn_weights_85_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = mul(x = attn_weights_85_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_2466_cast_fp16 = softmax(axis = var_1170, x = attn_weights_87_cast_fp16)[name = tensor("op_2466_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2462_cast_fp16, y = var_2466_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([1, 1280, 1, -1])]; + tensor input_181_cast_fp16 = reshape(shape = var_2470, x = attn_43_cast_fp16)[name = tensor("input_181_cast_fp16")]; + tensor var_2475 = const()[name = tensor("op_2475"), val = tensor([1, 1])]; + tensor var_2477 = const()[name = tensor("op_2477"), val = tensor([1, 1])]; + tensor var_2479_pad_type_0 = const()[name = tensor("op_2479_pad_type_0"), val = tensor("custom")]; + tensor var_2479_pad_0 = const()[name = tensor("op_2479_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241014208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242243072))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242243264)))]; + tensor var_2479_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_2477, groups = var_1186, pad = var_2479_pad_0, pad_type = var_2479_pad_type_0, strides = var_2475, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("op_2479_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = var_2479_cast_fp16, y = inputs_63_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor input_183_axes_0 = const()[name = tensor("input_183_axes_0"), val = tensor([1])]; + tensor input_183_gamma_0_to_fp16 = const()[name = tensor("input_183_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242245888)))]; + tensor input_183_beta_0_to_fp16 = const()[name = tensor("input_183_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242248512)))]; + tensor var_2489_to_fp16 = const()[name = tensor("op_2489_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = input_183_beta_0_to_fp16, epsilon = var_2489_to_fp16, gamma = input_183_gamma_0_to_fp16, x = inputs_65_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([1, 1])]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([1, 1])]; + tensor var_2509_pad_type_0 = const()[name = tensor("op_2509_pad_type_0"), val = tensor("custom")]; + tensor var_2509_pad_0 = const()[name = tensor("op_2509_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242251136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252081600))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252081792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252089536))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2509_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2507, groups = var_1186, pad = var_2509_pad_0, pad_type = var_2509_pad_type_0, strides = var_2505, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_183_cast_fp16)[name = tensor("op_2509_cast_fp16")]; + tensor var_2510_split_sizes_0 = const()[name = tensor("op_2510_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2510_axis_0 = const()[name = tensor("op_2510_axis_0"), val = tensor(1)]; + tensor var_2510_cast_fp16_0, tensor var_2510_cast_fp16_1 = split(axis = var_2510_axis_0, split_sizes = var_2510_split_sizes_0, x = var_2509_cast_fp16)[name = tensor("op_2510_cast_fp16")]; + tensor var_2512_mode_0 = const()[name = tensor("op_2512_mode_0"), val = tensor("EXACT")]; + tensor var_2512_cast_fp16 = gelu(mode = var_2512_mode_0, x = var_2510_cast_fp16_1)[name = tensor("op_2512_cast_fp16")]; + tensor input_185_cast_fp16 = mul(x = var_2510_cast_fp16_0, y = var_2512_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor var_2516 = const()[name = tensor("op_2516"), val = tensor([1, 1])]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([1, 1])]; + tensor var_2520_pad_type_0 = const()[name = tensor("op_2520_pad_type_0"), val = tensor("custom")]; + tensor var_2520_pad_0 = const()[name = tensor("op_2520_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252089728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257004992))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257005184)))]; + tensor var_2520_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_2518, groups = var_1186, pad = var_2520_pad_0, pad_type = var_2520_pad_type_0, strides = var_2516, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = tensor("op_2520_cast_fp16")]; + tensor inputs_67_cast_fp16 = add(x = var_2520_cast_fp16, y = inputs_65_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor hidden_states_107_axes_0 = const()[name = tensor("hidden_states_107_axes_0"), val = tensor([1])]; + tensor hidden_states_107_gamma_0_to_fp16 = const()[name = tensor("hidden_states_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257007808)))]; + tensor hidden_states_107_beta_0_to_fp16 = const()[name = tensor("hidden_states_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257010432)))]; + tensor var_2536_to_fp16 = const()[name = tensor("op_2536_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_107_cast_fp16 = layer_norm(axes = hidden_states_107_axes_0, beta = hidden_states_107_beta_0_to_fp16, epsilon = var_2536_to_fp16, gamma = hidden_states_107_gamma_0_to_fp16, x = inputs_67_cast_fp16)[name = tensor("hidden_states_107_cast_fp16")]; + tensor var_2551 = const()[name = tensor("op_2551"), val = tensor([1, 1])]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257013056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258241920))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_45_cast_fp16 = conv(dilations = var_2553, groups = var_1186, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2551, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_107_cast_fp16)[name = tensor("q_45_cast_fp16")]; + tensor var_2557 = const()[name = tensor("op_2557"), val = tensor([1, 1])]; + tensor var_2559 = const()[name = tensor("op_2559"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258242112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259470976))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_45_cast_fp16 = conv(dilations = var_2559, groups = var_1186, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2557, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_107_cast_fp16)[name = tensor("k_45_cast_fp16")]; + tensor var_2563 = const()[name = tensor("op_2563"), val = tensor([1, 1])]; + tensor var_2565 = const()[name = tensor("op_2565"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259471168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260700032))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_45_cast_fp16 = conv(dilations = var_2565, groups = var_1186, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2563, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_107_cast_fp16)[name = tensor("v_45_cast_fp16")]; + tensor var_2569 = const()[name = tensor("op_2569"), val = tensor([1, 20, 64, -1])]; + tensor var_2570_cast_fp16 = reshape(shape = var_2569, x = q_45_cast_fp16)[name = tensor("op_2570_cast_fp16")]; + tensor var_2571 = const()[name = tensor("op_2571"), val = tensor([1, 20, 64, -1])]; + tensor var_2572_cast_fp16 = reshape(shape = var_2571, x = k_45_cast_fp16)[name = tensor("op_2572_cast_fp16")]; + tensor var_2573 = const()[name = tensor("op_2573"), val = tensor([1, 20, 64, -1])]; + tensor var_2574_cast_fp16 = reshape(shape = var_2573, x = v_45_cast_fp16)[name = tensor("op_2574_cast_fp16")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2570_cast_fp16, y = var_2572_cast_fp16)[name = tensor("attn_weights_89_cast_fp16")]; + tensor attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_2578_cast_fp16 = softmax(axis = var_1170, x = attn_weights_91_cast_fp16)[name = tensor("op_2578_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2574_cast_fp16, y = var_2578_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_2582 = const()[name = tensor("op_2582"), val = tensor([1, 1280, 1, -1])]; + tensor input_187_cast_fp16 = reshape(shape = var_2582, x = attn_45_cast_fp16)[name = tensor("input_187_cast_fp16")]; + tensor var_2587 = const()[name = tensor("op_2587"), val = tensor([1, 1])]; + tensor var_2589 = const()[name = tensor("op_2589"), val = tensor([1, 1])]; + tensor var_2591_pad_type_0 = const()[name = tensor("op_2591_pad_type_0"), val = tensor("custom")]; + tensor var_2591_pad_0 = const()[name = tensor("op_2591_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260700224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261929088))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261929280)))]; + tensor var_2591_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_2589, groups = var_1186, pad = var_2591_pad_0, pad_type = var_2591_pad_type_0, strides = var_2587, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = tensor("op_2591_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = var_2591_cast_fp16, y = inputs_67_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor hidden_states_109_axes_0 = const()[name = tensor("hidden_states_109_axes_0"), val = tensor([1])]; + tensor hidden_states_109_gamma_0_to_fp16 = const()[name = tensor("hidden_states_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261931904)))]; + tensor hidden_states_109_beta_0_to_fp16 = const()[name = tensor("hidden_states_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261934528)))]; + tensor var_2601_to_fp16 = const()[name = tensor("op_2601_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_109_cast_fp16 = layer_norm(axes = hidden_states_109_axes_0, beta = hidden_states_109_beta_0_to_fp16, epsilon = var_2601_to_fp16, gamma = hidden_states_109_gamma_0_to_fp16, x = inputs_69_cast_fp16)[name = tensor("hidden_states_109_cast_fp16")]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor var_2618 = const()[name = tensor("op_2618"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261937152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263166016))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_47_cast_fp16 = conv(dilations = var_2618, groups = var_1186, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2616, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_109_cast_fp16)[name = tensor("q_47_cast_fp16")]; + tensor var_2622 = const()[name = tensor("op_2622"), val = tensor([1, 1])]; + tensor var_2624 = const()[name = tensor("op_2624"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263166208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265132352))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_47_cast_fp16 = conv(dilations = var_2624, groups = var_1186, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2622, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_47_cast_fp16")]; + tensor var_2628 = const()[name = tensor("op_2628"), val = tensor([1, 1])]; + tensor var_2630 = const()[name = tensor("op_2630"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265132544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267098688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_47_cast_fp16 = conv(dilations = var_2630, groups = var_1186, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2628, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_47_cast_fp16")]; + tensor var_2634 = const()[name = tensor("op_2634"), val = tensor([1, 20, 64, -1])]; + tensor var_2635_cast_fp16 = reshape(shape = var_2634, x = q_47_cast_fp16)[name = tensor("op_2635_cast_fp16")]; + tensor var_2636 = const()[name = tensor("op_2636"), val = tensor([1, 20, 64, -1])]; + tensor var_2637_cast_fp16 = reshape(shape = var_2636, x = k_47_cast_fp16)[name = tensor("op_2637_cast_fp16")]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([1, 20, 64, -1])]; + tensor var_2639_cast_fp16 = reshape(shape = var_2638, x = v_47_cast_fp16)[name = tensor("op_2639_cast_fp16")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast_fp16 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2635_cast_fp16, y = var_2637_cast_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor attn_weights_95_cast_fp16 = mul(x = attn_weights_93_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_95_cast_fp16")]; + tensor var_2643_cast_fp16 = softmax(axis = var_1170, x = attn_weights_95_cast_fp16)[name = tensor("op_2643_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2639_cast_fp16, y = var_2643_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_2647 = const()[name = tensor("op_2647"), val = tensor([1, 1280, 1, -1])]; + tensor input_189_cast_fp16 = reshape(shape = var_2647, x = attn_47_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor var_2652 = const()[name = tensor("op_2652"), val = tensor([1, 1])]; + tensor var_2654 = const()[name = tensor("op_2654"), val = tensor([1, 1])]; + tensor var_2656_pad_type_0 = const()[name = tensor("op_2656_pad_type_0"), val = tensor("custom")]; + tensor var_2656_pad_0 = const()[name = tensor("op_2656_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267098880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268327744))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268327936)))]; + tensor var_2656_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_2654, groups = var_1186, pad = var_2656_pad_0, pad_type = var_2656_pad_type_0, strides = var_2652, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = tensor("op_2656_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = var_2656_cast_fp16, y = inputs_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor input_191_axes_0 = const()[name = tensor("input_191_axes_0"), val = tensor([1])]; + tensor input_191_gamma_0_to_fp16 = const()[name = tensor("input_191_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268330560)))]; + tensor input_191_beta_0_to_fp16 = const()[name = tensor("input_191_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268333184)))]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_191_cast_fp16 = layer_norm(axes = input_191_axes_0, beta = input_191_beta_0_to_fp16, epsilon = var_2666_to_fp16, gamma = input_191_gamma_0_to_fp16, x = inputs_71_cast_fp16)[name = tensor("input_191_cast_fp16")]; + tensor var_2682 = const()[name = tensor("op_2682"), val = tensor([1, 1])]; + tensor var_2684 = const()[name = tensor("op_2684"), val = tensor([1, 1])]; + tensor var_2686_pad_type_0 = const()[name = tensor("op_2686_pad_type_0"), val = tensor("custom")]; + tensor var_2686_pad_0 = const()[name = tensor("op_2686_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268335808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278166272))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278166464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278174208))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2686_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2684, groups = var_1186, pad = var_2686_pad_0, pad_type = var_2686_pad_type_0, strides = var_2682, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_191_cast_fp16)[name = tensor("op_2686_cast_fp16")]; + tensor var_2687_split_sizes_0 = const()[name = tensor("op_2687_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2687_axis_0 = const()[name = tensor("op_2687_axis_0"), val = tensor(1)]; + tensor var_2687_cast_fp16_0, tensor var_2687_cast_fp16_1 = split(axis = var_2687_axis_0, split_sizes = var_2687_split_sizes_0, x = var_2686_cast_fp16)[name = tensor("op_2687_cast_fp16")]; + tensor var_2689_mode_0 = const()[name = tensor("op_2689_mode_0"), val = tensor("EXACT")]; + tensor var_2689_cast_fp16 = gelu(mode = var_2689_mode_0, x = var_2687_cast_fp16_1)[name = tensor("op_2689_cast_fp16")]; + tensor input_193_cast_fp16 = mul(x = var_2687_cast_fp16_0, y = var_2689_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor var_2697_pad_type_0 = const()[name = tensor("op_2697_pad_type_0"), val = tensor("custom")]; + tensor var_2697_pad_0 = const()[name = tensor("op_2697_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278174400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283089664))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283089856)))]; + tensor var_2697_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_2695, groups = var_1186, pad = var_2697_pad_0, pad_type = var_2697_pad_type_0, strides = var_2693, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = tensor("op_2697_cast_fp16")]; + tensor inputs_73_cast_fp16 = add(x = var_2697_cast_fp16, y = inputs_71_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor hidden_states_113_axes_0 = const()[name = tensor("hidden_states_113_axes_0"), val = tensor([1])]; + tensor hidden_states_113_gamma_0_to_fp16 = const()[name = tensor("hidden_states_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283092480)))]; + tensor hidden_states_113_beta_0_to_fp16 = const()[name = tensor("hidden_states_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283095104)))]; + tensor var_2713_to_fp16 = const()[name = tensor("op_2713_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_113_cast_fp16 = layer_norm(axes = hidden_states_113_axes_0, beta = hidden_states_113_beta_0_to_fp16, epsilon = var_2713_to_fp16, gamma = hidden_states_113_gamma_0_to_fp16, x = inputs_73_cast_fp16)[name = tensor("hidden_states_113_cast_fp16")]; + tensor var_2728 = const()[name = tensor("op_2728"), val = tensor([1, 1])]; + tensor var_2730 = const()[name = tensor("op_2730"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283097728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284326592))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_49_cast_fp16 = conv(dilations = var_2730, groups = var_1186, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2728, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_113_cast_fp16)[name = tensor("q_49_cast_fp16")]; + tensor var_2734 = const()[name = tensor("op_2734"), val = tensor([1, 1])]; + tensor var_2736 = const()[name = tensor("op_2736"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284326784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285555648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_49_cast_fp16 = conv(dilations = var_2736, groups = var_1186, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2734, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_113_cast_fp16)[name = tensor("k_49_cast_fp16")]; + tensor var_2740 = const()[name = tensor("op_2740"), val = tensor([1, 1])]; + tensor var_2742 = const()[name = tensor("op_2742"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285555840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286784704))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_49_cast_fp16 = conv(dilations = var_2742, groups = var_1186, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2740, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_113_cast_fp16)[name = tensor("v_49_cast_fp16")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 20, 64, -1])]; + tensor var_2747_cast_fp16 = reshape(shape = var_2746, x = q_49_cast_fp16)[name = tensor("op_2747_cast_fp16")]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([1, 20, 64, -1])]; + tensor var_2749_cast_fp16 = reshape(shape = var_2748, x = k_49_cast_fp16)[name = tensor("op_2749_cast_fp16")]; + tensor var_2750 = const()[name = tensor("op_2750"), val = tensor([1, 20, 64, -1])]; + tensor var_2751_cast_fp16 = reshape(shape = var_2750, x = v_49_cast_fp16)[name = tensor("op_2751_cast_fp16")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2747_cast_fp16, y = var_2749_cast_fp16)[name = tensor("attn_weights_97_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_2755_cast_fp16 = softmax(axis = var_1170, x = attn_weights_99_cast_fp16)[name = tensor("op_2755_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2751_cast_fp16, y = var_2755_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_2759 = const()[name = tensor("op_2759"), val = tensor([1, 1280, 1, -1])]; + tensor input_195_cast_fp16 = reshape(shape = var_2759, x = attn_49_cast_fp16)[name = tensor("input_195_cast_fp16")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([1, 1])]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([1, 1])]; + tensor var_2768_pad_type_0 = const()[name = tensor("op_2768_pad_type_0"), val = tensor("custom")]; + tensor var_2768_pad_0 = const()[name = tensor("op_2768_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286784896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288013760))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288013952)))]; + tensor var_2768_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_2766, groups = var_1186, pad = var_2768_pad_0, pad_type = var_2768_pad_type_0, strides = var_2764, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_195_cast_fp16)[name = tensor("op_2768_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = var_2768_cast_fp16, y = inputs_73_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor hidden_states_115_axes_0 = const()[name = tensor("hidden_states_115_axes_0"), val = tensor([1])]; + tensor hidden_states_115_gamma_0_to_fp16 = const()[name = tensor("hidden_states_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288016576)))]; + tensor hidden_states_115_beta_0_to_fp16 = const()[name = tensor("hidden_states_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288019200)))]; + tensor var_2778_to_fp16 = const()[name = tensor("op_2778_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_115_cast_fp16 = layer_norm(axes = hidden_states_115_axes_0, beta = hidden_states_115_beta_0_to_fp16, epsilon = var_2778_to_fp16, gamma = hidden_states_115_gamma_0_to_fp16, x = inputs_75_cast_fp16)[name = tensor("hidden_states_115_cast_fp16")]; + tensor var_2793 = const()[name = tensor("op_2793"), val = tensor([1, 1])]; + tensor var_2795 = const()[name = tensor("op_2795"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(288021824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289250688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_51_cast_fp16 = conv(dilations = var_2795, groups = var_1186, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2793, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_115_cast_fp16)[name = tensor("q_51_cast_fp16")]; + tensor var_2799 = const()[name = tensor("op_2799"), val = tensor([1, 1])]; + tensor var_2801 = const()[name = tensor("op_2801"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289250880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291217024))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_51_cast_fp16 = conv(dilations = var_2801, groups = var_1186, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2799, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_51_cast_fp16")]; + tensor var_2805 = const()[name = tensor("op_2805"), val = tensor([1, 1])]; + tensor var_2807 = const()[name = tensor("op_2807"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291217216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293183360))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_51_cast_fp16 = conv(dilations = var_2807, groups = var_1186, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2805, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_51_cast_fp16")]; + tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([1, 20, 64, -1])]; + tensor var_2812_cast_fp16 = reshape(shape = var_2811, x = q_51_cast_fp16)[name = tensor("op_2812_cast_fp16")]; + tensor var_2813 = const()[name = tensor("op_2813"), val = tensor([1, 20, 64, -1])]; + tensor var_2814_cast_fp16 = reshape(shape = var_2813, x = k_51_cast_fp16)[name = tensor("op_2814_cast_fp16")]; + tensor var_2815 = const()[name = tensor("op_2815"), val = tensor([1, 20, 64, -1])]; + tensor var_2816_cast_fp16 = reshape(shape = var_2815, x = v_51_cast_fp16)[name = tensor("op_2816_cast_fp16")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast_fp16 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2812_cast_fp16, y = var_2814_cast_fp16)[name = tensor("attn_weights_101_cast_fp16")]; + tensor attn_weights_103_cast_fp16 = mul(x = attn_weights_101_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_2820_cast_fp16 = softmax(axis = var_1170, x = attn_weights_103_cast_fp16)[name = tensor("op_2820_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2816_cast_fp16, y = var_2820_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_2824 = const()[name = tensor("op_2824"), val = tensor([1, 1280, 1, -1])]; + tensor input_197_cast_fp16 = reshape(shape = var_2824, x = attn_51_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([1, 1])]; + tensor var_2831 = const()[name = tensor("op_2831"), val = tensor([1, 1])]; + tensor var_2833_pad_type_0 = const()[name = tensor("op_2833_pad_type_0"), val = tensor("custom")]; + tensor var_2833_pad_0 = const()[name = tensor("op_2833_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293183552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294412416))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294412608)))]; + tensor var_2833_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_2831, groups = var_1186, pad = var_2833_pad_0, pad_type = var_2833_pad_type_0, strides = var_2829, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("op_2833_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = var_2833_cast_fp16, y = inputs_75_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor input_199_axes_0 = const()[name = tensor("input_199_axes_0"), val = tensor([1])]; + tensor input_199_gamma_0_to_fp16 = const()[name = tensor("input_199_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294415232)))]; + tensor input_199_beta_0_to_fp16 = const()[name = tensor("input_199_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294417856)))]; + tensor var_2843_to_fp16 = const()[name = tensor("op_2843_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_199_cast_fp16 = layer_norm(axes = input_199_axes_0, beta = input_199_beta_0_to_fp16, epsilon = var_2843_to_fp16, gamma = input_199_gamma_0_to_fp16, x = inputs_77_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor var_2859 = const()[name = tensor("op_2859"), val = tensor([1, 1])]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863_pad_type_0 = const()[name = tensor("op_2863_pad_type_0"), val = tensor("custom")]; + tensor var_2863_pad_0 = const()[name = tensor("op_2863_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294420480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304250944))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304251136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304258880))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2863_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2861, groups = var_1186, pad = var_2863_pad_0, pad_type = var_2863_pad_type_0, strides = var_2859, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = tensor("op_2863_cast_fp16")]; + tensor var_2864_split_sizes_0 = const()[name = tensor("op_2864_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2864_axis_0 = const()[name = tensor("op_2864_axis_0"), val = tensor(1)]; + tensor var_2864_cast_fp16_0, tensor var_2864_cast_fp16_1 = split(axis = var_2864_axis_0, split_sizes = var_2864_split_sizes_0, x = var_2863_cast_fp16)[name = tensor("op_2864_cast_fp16")]; + tensor var_2866_mode_0 = const()[name = tensor("op_2866_mode_0"), val = tensor("EXACT")]; + tensor var_2866_cast_fp16 = gelu(mode = var_2866_mode_0, x = var_2864_cast_fp16_1)[name = tensor("op_2866_cast_fp16")]; + tensor input_201_cast_fp16 = mul(x = var_2864_cast_fp16_0, y = var_2866_cast_fp16)[name = tensor("input_201_cast_fp16")]; + tensor var_2870 = const()[name = tensor("op_2870"), val = tensor([1, 1])]; + tensor var_2872 = const()[name = tensor("op_2872"), val = tensor([1, 1])]; + tensor var_2874_pad_type_0 = const()[name = tensor("op_2874_pad_type_0"), val = tensor("custom")]; + tensor var_2874_pad_0 = const()[name = tensor("op_2874_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304259072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309174336))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309174528)))]; + tensor var_2874_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_2872, groups = var_1186, pad = var_2874_pad_0, pad_type = var_2874_pad_type_0, strides = var_2870, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = tensor("op_2874_cast_fp16")]; + tensor inputs_79_cast_fp16 = add(x = var_2874_cast_fp16, y = inputs_77_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor hidden_states_119_axes_0 = const()[name = tensor("hidden_states_119_axes_0"), val = tensor([1])]; + tensor hidden_states_119_gamma_0_to_fp16 = const()[name = tensor("hidden_states_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309177152)))]; + tensor hidden_states_119_beta_0_to_fp16 = const()[name = tensor("hidden_states_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309179776)))]; + tensor var_2890_to_fp16 = const()[name = tensor("op_2890_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_119_cast_fp16 = layer_norm(axes = hidden_states_119_axes_0, beta = hidden_states_119_beta_0_to_fp16, epsilon = var_2890_to_fp16, gamma = hidden_states_119_gamma_0_to_fp16, x = inputs_79_cast_fp16)[name = tensor("hidden_states_119_cast_fp16")]; + tensor var_2905 = const()[name = tensor("op_2905"), val = tensor([1, 1])]; + tensor var_2907 = const()[name = tensor("op_2907"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309182400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310411264))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_53_cast_fp16 = conv(dilations = var_2907, groups = var_1186, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2905, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_119_cast_fp16)[name = tensor("q_53_cast_fp16")]; + tensor var_2911 = const()[name = tensor("op_2911"), val = tensor([1, 1])]; + tensor var_2913 = const()[name = tensor("op_2913"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310411456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311640320))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_53_cast_fp16 = conv(dilations = var_2913, groups = var_1186, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2911, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_119_cast_fp16)[name = tensor("k_53_cast_fp16")]; + tensor var_2917 = const()[name = tensor("op_2917"), val = tensor([1, 1])]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311640512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312869376))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_53_cast_fp16 = conv(dilations = var_2919, groups = var_1186, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2917, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_119_cast_fp16)[name = tensor("v_53_cast_fp16")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1, 20, 64, -1])]; + tensor var_2924_cast_fp16 = reshape(shape = var_2923, x = q_53_cast_fp16)[name = tensor("op_2924_cast_fp16")]; + tensor var_2925 = const()[name = tensor("op_2925"), val = tensor([1, 20, 64, -1])]; + tensor var_2926_cast_fp16 = reshape(shape = var_2925, x = k_53_cast_fp16)[name = tensor("op_2926_cast_fp16")]; + tensor var_2927 = const()[name = tensor("op_2927"), val = tensor([1, 20, 64, -1])]; + tensor var_2928_cast_fp16 = reshape(shape = var_2927, x = v_53_cast_fp16)[name = tensor("op_2928_cast_fp16")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_2924_cast_fp16, y = var_2926_cast_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_107_cast_fp16")]; + tensor var_2932_cast_fp16 = softmax(axis = var_1170, x = attn_weights_107_cast_fp16)[name = tensor("op_2932_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_2928_cast_fp16, y = var_2932_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_2936 = const()[name = tensor("op_2936"), val = tensor([1, 1280, 1, -1])]; + tensor input_203_cast_fp16 = reshape(shape = var_2936, x = attn_53_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor var_2941 = const()[name = tensor("op_2941"), val = tensor([1, 1])]; + tensor var_2943 = const()[name = tensor("op_2943"), val = tensor([1, 1])]; + tensor var_2945_pad_type_0 = const()[name = tensor("op_2945_pad_type_0"), val = tensor("custom")]; + tensor var_2945_pad_0 = const()[name = tensor("op_2945_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312869568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314098432))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314098624)))]; + tensor var_2945_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_2943, groups = var_1186, pad = var_2945_pad_0, pad_type = var_2945_pad_type_0, strides = var_2941, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_203_cast_fp16)[name = tensor("op_2945_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = var_2945_cast_fp16, y = inputs_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor hidden_states_121_axes_0 = const()[name = tensor("hidden_states_121_axes_0"), val = tensor([1])]; + tensor hidden_states_121_gamma_0_to_fp16 = const()[name = tensor("hidden_states_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314101248)))]; + tensor hidden_states_121_beta_0_to_fp16 = const()[name = tensor("hidden_states_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314103872)))]; + tensor var_2955_to_fp16 = const()[name = tensor("op_2955_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_121_cast_fp16 = layer_norm(axes = hidden_states_121_axes_0, beta = hidden_states_121_beta_0_to_fp16, epsilon = var_2955_to_fp16, gamma = hidden_states_121_gamma_0_to_fp16, x = inputs_81_cast_fp16)[name = tensor("hidden_states_121_cast_fp16")]; + tensor var_2970 = const()[name = tensor("op_2970"), val = tensor([1, 1])]; + tensor var_2972 = const()[name = tensor("op_2972"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314106496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315335360))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_55_cast_fp16 = conv(dilations = var_2972, groups = var_1186, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_2970, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_121_cast_fp16)[name = tensor("q_55_cast_fp16")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315335552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317301696))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_55_cast_fp16 = conv(dilations = var_2978, groups = var_1186, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_2976, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_55_cast_fp16")]; + tensor var_2982 = const()[name = tensor("op_2982"), val = tensor([1, 1])]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317301888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319268032))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_55_cast_fp16 = conv(dilations = var_2984, groups = var_1186, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_2982, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_55_cast_fp16")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1, 20, 64, -1])]; + tensor var_2989_cast_fp16 = reshape(shape = var_2988, x = q_55_cast_fp16)[name = tensor("op_2989_cast_fp16")]; + tensor var_2990 = const()[name = tensor("op_2990"), val = tensor([1, 20, 64, -1])]; + tensor var_2991_cast_fp16 = reshape(shape = var_2990, x = k_55_cast_fp16)[name = tensor("op_2991_cast_fp16")]; + tensor var_2992 = const()[name = tensor("op_2992"), val = tensor([1, 20, 64, -1])]; + tensor var_2993_cast_fp16 = reshape(shape = var_2992, x = v_55_cast_fp16)[name = tensor("op_2993_cast_fp16")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_2989_cast_fp16, y = var_2991_cast_fp16)[name = tensor("attn_weights_109_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = mul(x = attn_weights_109_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_2997_cast_fp16 = softmax(axis = var_1170, x = attn_weights_111_cast_fp16)[name = tensor("op_2997_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_2993_cast_fp16, y = var_2997_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_3001 = const()[name = tensor("op_3001"), val = tensor([1, 1280, 1, -1])]; + tensor input_205_cast_fp16 = reshape(shape = var_3001, x = attn_55_cast_fp16)[name = tensor("input_205_cast_fp16")]; + tensor var_3006 = const()[name = tensor("op_3006"), val = tensor([1, 1])]; + tensor var_3008 = const()[name = tensor("op_3008"), val = tensor([1, 1])]; + tensor var_3010_pad_type_0 = const()[name = tensor("op_3010_pad_type_0"), val = tensor("custom")]; + tensor var_3010_pad_0 = const()[name = tensor("op_3010_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319268224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320497088))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320497280)))]; + tensor var_3010_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_3008, groups = var_1186, pad = var_3010_pad_0, pad_type = var_3010_pad_type_0, strides = var_3006, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = tensor("op_3010_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = var_3010_cast_fp16, y = inputs_81_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor input_207_axes_0 = const()[name = tensor("input_207_axes_0"), val = tensor([1])]; + tensor input_207_gamma_0_to_fp16 = const()[name = tensor("input_207_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320499904)))]; + tensor input_207_beta_0_to_fp16 = const()[name = tensor("input_207_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320502528)))]; + tensor var_3020_to_fp16 = const()[name = tensor("op_3020_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_207_cast_fp16 = layer_norm(axes = input_207_axes_0, beta = input_207_beta_0_to_fp16, epsilon = var_3020_to_fp16, gamma = input_207_gamma_0_to_fp16, x = inputs_83_cast_fp16)[name = tensor("input_207_cast_fp16")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([1, 1])]; + tensor var_3038 = const()[name = tensor("op_3038"), val = tensor([1, 1])]; + tensor var_3040_pad_type_0 = const()[name = tensor("op_3040_pad_type_0"), val = tensor("custom")]; + tensor var_3040_pad_0 = const()[name = tensor("op_3040_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320505152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330335616))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330335808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330343552))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3040_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3038, groups = var_1186, pad = var_3040_pad_0, pad_type = var_3040_pad_type_0, strides = var_3036, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_207_cast_fp16)[name = tensor("op_3040_cast_fp16")]; + tensor var_3041_split_sizes_0 = const()[name = tensor("op_3041_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3041_axis_0 = const()[name = tensor("op_3041_axis_0"), val = tensor(1)]; + tensor var_3041_cast_fp16_0, tensor var_3041_cast_fp16_1 = split(axis = var_3041_axis_0, split_sizes = var_3041_split_sizes_0, x = var_3040_cast_fp16)[name = tensor("op_3041_cast_fp16")]; + tensor var_3043_mode_0 = const()[name = tensor("op_3043_mode_0"), val = tensor("EXACT")]; + tensor var_3043_cast_fp16 = gelu(mode = var_3043_mode_0, x = var_3041_cast_fp16_1)[name = tensor("op_3043_cast_fp16")]; + tensor input_209_cast_fp16 = mul(x = var_3041_cast_fp16_0, y = var_3043_cast_fp16)[name = tensor("input_209_cast_fp16")]; + tensor var_3047 = const()[name = tensor("op_3047"), val = tensor([1, 1])]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1, 1])]; + tensor var_3051_pad_type_0 = const()[name = tensor("op_3051_pad_type_0"), val = tensor("custom")]; + tensor var_3051_pad_0 = const()[name = tensor("op_3051_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330343744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335259008))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335259200)))]; + tensor var_3051_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_3049, groups = var_1186, pad = var_3051_pad_0, pad_type = var_3051_pad_type_0, strides = var_3047, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_209_cast_fp16)[name = tensor("op_3051_cast_fp16")]; + tensor hidden_states_125_cast_fp16 = add(x = var_3051_cast_fp16, y = inputs_83_cast_fp16)[name = tensor("hidden_states_125_cast_fp16")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([1, 1280, 32, 32])]; + tensor input_211_cast_fp16 = reshape(shape = var_3053, x = hidden_states_125_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor var_3057 = const()[name = tensor("op_3057"), val = tensor([1, 1])]; + tensor var_3059 = const()[name = tensor("op_3059"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_type_0 = const()[name = tensor("hidden_states_127_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_127_pad_0 = const()[name = tensor("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335261824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336490688))), name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336490880)))]; + tensor hidden_states_127_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3059, groups = var_1186, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = var_3057, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_211_cast_fp16)[name = tensor("hidden_states_127_cast_fp16")]; + tensor input_213_cast_fp16 = add(x = hidden_states_127_cast_fp16, y = hidden_states_61_cast_fp16)[name = tensor("input_213_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_213_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336493504)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336496128)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor input_217_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_217_cast_fp16")]; + tensor var_3074 = const()[name = tensor("op_3074"), val = tensor([1, 1])]; + tensor var_3076 = const()[name = tensor("op_3076"), val = tensor([1, 1])]; + tensor hidden_states_129_pad_type_0 = const()[name = tensor("hidden_states_129_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_129_pad_0 = const()[name = tensor("hidden_states_129_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336498752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347558016))), name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347558208)))]; + tensor hidden_states_129_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3076, groups = var_1186, pad = hidden_states_129_pad_0, pad_type = hidden_states_129_pad_type_0, strides = var_3074, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = tensor("hidden_states_129_cast_fp16")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347560832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348789696))), name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348789888)))]; + tensor temb_11_cast_fp16 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3084, groups = var_1186, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_3082, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_11_cast_fp16")]; + tensor input_221_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = temb_11_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_221_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348792512)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348795136)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor input_225_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor var_3096 = const()[name = tensor("op_3096"), val = tensor([1, 1])]; + tensor hidden_states_131_pad_type_0 = const()[name = tensor("hidden_states_131_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_131_pad_0 = const()[name = tensor("hidden_states_131_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348797760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359857024))), name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359857216)))]; + tensor hidden_states_131_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3096, groups = var_1186, pad = hidden_states_131_pad_0, pad_type = hidden_states_131_pad_type_0, strides = var_3094, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = tensor("hidden_states_131_cast_fp16")]; + tensor hidden_states_133_cast_fp16 = add(x = input_213_cast_fp16, y = hidden_states_131_cast_fp16)[name = tensor("hidden_states_133_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = hidden_states_133_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359859840)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359862464)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor var_3134 = const()[name = tensor("op_3134"), val = tensor([1, 1])]; + tensor var_3136 = const()[name = tensor("op_3136"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359865088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361093952))), name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361094144)))]; + tensor hidden_states_135_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3136, groups = var_1186, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_3134, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_31_cast_fp16)[name = tensor("hidden_states_135_cast_fp16")]; + tensor var_3141 = const()[name = tensor("op_3141"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_85_cast_fp16 = reshape(shape = var_3141, x = hidden_states_135_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor hidden_states_137_axes_0 = const()[name = tensor("hidden_states_137_axes_0"), val = tensor([1])]; + tensor hidden_states_137_gamma_0_to_fp16 = const()[name = tensor("hidden_states_137_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361096768)))]; + tensor hidden_states_137_beta_0_to_fp16 = const()[name = tensor("hidden_states_137_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361099392)))]; + tensor var_3157_to_fp16 = const()[name = tensor("op_3157_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_137_cast_fp16 = layer_norm(axes = hidden_states_137_axes_0, beta = hidden_states_137_beta_0_to_fp16, epsilon = var_3157_to_fp16, gamma = hidden_states_137_gamma_0_to_fp16, x = inputs_85_cast_fp16)[name = tensor("hidden_states_137_cast_fp16")]; + tensor var_3172 = const()[name = tensor("op_3172"), val = tensor([1, 1])]; + tensor var_3174 = const()[name = tensor("op_3174"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361102016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362330880))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_57_cast_fp16 = conv(dilations = var_3174, groups = var_1186, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3172, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_137_cast_fp16)[name = tensor("q_57_cast_fp16")]; + tensor var_3178 = const()[name = tensor("op_3178"), val = tensor([1, 1])]; + tensor var_3180 = const()[name = tensor("op_3180"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362331072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(363559936))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_57_cast_fp16 = conv(dilations = var_3180, groups = var_1186, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3178, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_137_cast_fp16)[name = tensor("k_57_cast_fp16")]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor var_3186 = const()[name = tensor("op_3186"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(363560128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364788992))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_57_cast_fp16 = conv(dilations = var_3186, groups = var_1186, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3184, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_137_cast_fp16)[name = tensor("v_57_cast_fp16")]; + tensor var_3190 = const()[name = tensor("op_3190"), val = tensor([1, 20, 64, -1])]; + tensor var_3191_cast_fp16 = reshape(shape = var_3190, x = q_57_cast_fp16)[name = tensor("op_3191_cast_fp16")]; + tensor var_3192 = const()[name = tensor("op_3192"), val = tensor([1, 20, 64, -1])]; + tensor var_3193_cast_fp16 = reshape(shape = var_3192, x = k_57_cast_fp16)[name = tensor("op_3193_cast_fp16")]; + tensor var_3194 = const()[name = tensor("op_3194"), val = tensor([1, 20, 64, -1])]; + tensor var_3195_cast_fp16 = reshape(shape = var_3194, x = v_57_cast_fp16)[name = tensor("op_3195_cast_fp16")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3191_cast_fp16, y = var_3193_cast_fp16)[name = tensor("attn_weights_113_cast_fp16")]; + tensor attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_3199_cast_fp16 = softmax(axis = var_1170, x = attn_weights_115_cast_fp16)[name = tensor("op_3199_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3195_cast_fp16, y = var_3199_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_3203 = const()[name = tensor("op_3203"), val = tensor([1, 1280, 1, -1])]; + tensor input_229_cast_fp16 = reshape(shape = var_3203, x = attn_57_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor var_3208 = const()[name = tensor("op_3208"), val = tensor([1, 1])]; + tensor var_3210 = const()[name = tensor("op_3210"), val = tensor([1, 1])]; + tensor var_3212_pad_type_0 = const()[name = tensor("op_3212_pad_type_0"), val = tensor("custom")]; + tensor var_3212_pad_0 = const()[name = tensor("op_3212_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364789184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366018048))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366018240)))]; + tensor var_3212_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3210, groups = var_1186, pad = var_3212_pad_0, pad_type = var_3212_pad_type_0, strides = var_3208, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = tensor("op_3212_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = var_3212_cast_fp16, y = inputs_85_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor hidden_states_139_axes_0 = const()[name = tensor("hidden_states_139_axes_0"), val = tensor([1])]; + tensor hidden_states_139_gamma_0_to_fp16 = const()[name = tensor("hidden_states_139_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366020864)))]; + tensor hidden_states_139_beta_0_to_fp16 = const()[name = tensor("hidden_states_139_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366023488)))]; + tensor var_3222_to_fp16 = const()[name = tensor("op_3222_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_139_cast_fp16 = layer_norm(axes = hidden_states_139_axes_0, beta = hidden_states_139_beta_0_to_fp16, epsilon = var_3222_to_fp16, gamma = hidden_states_139_gamma_0_to_fp16, x = inputs_87_cast_fp16)[name = tensor("hidden_states_139_cast_fp16")]; + tensor var_3237 = const()[name = tensor("op_3237"), val = tensor([1, 1])]; + tensor var_3239 = const()[name = tensor("op_3239"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366026112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367254976))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_59_cast_fp16 = conv(dilations = var_3239, groups = var_1186, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3237, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_139_cast_fp16)[name = tensor("q_59_cast_fp16")]; + tensor var_3243 = const()[name = tensor("op_3243"), val = tensor([1, 1])]; + tensor var_3245 = const()[name = tensor("op_3245"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367255168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369221312))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_59_cast_fp16 = conv(dilations = var_3245, groups = var_1186, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3243, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_59_cast_fp16")]; + tensor var_3249 = const()[name = tensor("op_3249"), val = tensor([1, 1])]; + tensor var_3251 = const()[name = tensor("op_3251"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369221504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371187648))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_59_cast_fp16 = conv(dilations = var_3251, groups = var_1186, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3249, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_59_cast_fp16")]; + tensor var_3255 = const()[name = tensor("op_3255"), val = tensor([1, 20, 64, -1])]; + tensor var_3256_cast_fp16 = reshape(shape = var_3255, x = q_59_cast_fp16)[name = tensor("op_3256_cast_fp16")]; + tensor var_3257 = const()[name = tensor("op_3257"), val = tensor([1, 20, 64, -1])]; + tensor var_3258_cast_fp16 = reshape(shape = var_3257, x = k_59_cast_fp16)[name = tensor("op_3258_cast_fp16")]; + tensor var_3259 = const()[name = tensor("op_3259"), val = tensor([1, 20, 64, -1])]; + tensor var_3260_cast_fp16 = reshape(shape = var_3259, x = v_59_cast_fp16)[name = tensor("op_3260_cast_fp16")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast_fp16 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3256_cast_fp16, y = var_3258_cast_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor attn_weights_119_cast_fp16 = mul(x = attn_weights_117_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_119_cast_fp16")]; + tensor var_3264_cast_fp16 = softmax(axis = var_1170, x = attn_weights_119_cast_fp16)[name = tensor("op_3264_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3260_cast_fp16, y = var_3264_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_3268 = const()[name = tensor("op_3268"), val = tensor([1, 1280, 1, -1])]; + tensor input_231_cast_fp16 = reshape(shape = var_3268, x = attn_59_cast_fp16)[name = tensor("input_231_cast_fp16")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([1, 1])]; + tensor var_3275 = const()[name = tensor("op_3275"), val = tensor([1, 1])]; + tensor var_3277_pad_type_0 = const()[name = tensor("op_3277_pad_type_0"), val = tensor("custom")]; + tensor var_3277_pad_0 = const()[name = tensor("op_3277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(371187840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372416704))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372416896)))]; + tensor var_3277_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3275, groups = var_1186, pad = var_3277_pad_0, pad_type = var_3277_pad_type_0, strides = var_3273, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_231_cast_fp16)[name = tensor("op_3277_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = var_3277_cast_fp16, y = inputs_87_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor input_233_axes_0 = const()[name = tensor("input_233_axes_0"), val = tensor([1])]; + tensor input_233_gamma_0_to_fp16 = const()[name = tensor("input_233_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372419520)))]; + tensor input_233_beta_0_to_fp16 = const()[name = tensor("input_233_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372422144)))]; + tensor var_3287_to_fp16 = const()[name = tensor("op_3287_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_233_cast_fp16 = layer_norm(axes = input_233_axes_0, beta = input_233_beta_0_to_fp16, epsilon = var_3287_to_fp16, gamma = input_233_gamma_0_to_fp16, x = inputs_89_cast_fp16)[name = tensor("input_233_cast_fp16")]; + tensor var_3303 = const()[name = tensor("op_3303"), val = tensor([1, 1])]; + tensor var_3305 = const()[name = tensor("op_3305"), val = tensor([1, 1])]; + tensor var_3307_pad_type_0 = const()[name = tensor("op_3307_pad_type_0"), val = tensor("custom")]; + tensor var_3307_pad_0 = const()[name = tensor("op_3307_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372424768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382255232))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382255424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382263168))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3307_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3305, groups = var_1186, pad = var_3307_pad_0, pad_type = var_3307_pad_type_0, strides = var_3303, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_233_cast_fp16)[name = tensor("op_3307_cast_fp16")]; + tensor var_3308_split_sizes_0 = const()[name = tensor("op_3308_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3308_axis_0 = const()[name = tensor("op_3308_axis_0"), val = tensor(1)]; + tensor var_3308_cast_fp16_0, tensor var_3308_cast_fp16_1 = split(axis = var_3308_axis_0, split_sizes = var_3308_split_sizes_0, x = var_3307_cast_fp16)[name = tensor("op_3308_cast_fp16")]; + tensor var_3310_mode_0 = const()[name = tensor("op_3310_mode_0"), val = tensor("EXACT")]; + tensor var_3310_cast_fp16 = gelu(mode = var_3310_mode_0, x = var_3308_cast_fp16_1)[name = tensor("op_3310_cast_fp16")]; + tensor input_235_cast_fp16 = mul(x = var_3308_cast_fp16_0, y = var_3310_cast_fp16)[name = tensor("input_235_cast_fp16")]; + tensor var_3314 = const()[name = tensor("op_3314"), val = tensor([1, 1])]; + tensor var_3316 = const()[name = tensor("op_3316"), val = tensor([1, 1])]; + tensor var_3318_pad_type_0 = const()[name = tensor("op_3318_pad_type_0"), val = tensor("custom")]; + tensor var_3318_pad_0 = const()[name = tensor("op_3318_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382263360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387178624))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387178816)))]; + tensor var_3318_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3316, groups = var_1186, pad = var_3318_pad_0, pad_type = var_3318_pad_type_0, strides = var_3314, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_235_cast_fp16)[name = tensor("op_3318_cast_fp16")]; + tensor inputs_91_cast_fp16 = add(x = var_3318_cast_fp16, y = inputs_89_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor hidden_states_143_axes_0 = const()[name = tensor("hidden_states_143_axes_0"), val = tensor([1])]; + tensor hidden_states_143_gamma_0_to_fp16 = const()[name = tensor("hidden_states_143_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387181440)))]; + tensor hidden_states_143_beta_0_to_fp16 = const()[name = tensor("hidden_states_143_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387184064)))]; + tensor var_3334_to_fp16 = const()[name = tensor("op_3334_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_143_cast_fp16 = layer_norm(axes = hidden_states_143_axes_0, beta = hidden_states_143_beta_0_to_fp16, epsilon = var_3334_to_fp16, gamma = hidden_states_143_gamma_0_to_fp16, x = inputs_91_cast_fp16)[name = tensor("hidden_states_143_cast_fp16")]; + tensor var_3349 = const()[name = tensor("op_3349"), val = tensor([1, 1])]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(387186688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(388415552))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_61_cast_fp16 = conv(dilations = var_3351, groups = var_1186, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3349, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_143_cast_fp16)[name = tensor("q_61_cast_fp16")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1, 1])]; + tensor var_3357 = const()[name = tensor("op_3357"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(388415744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389644608))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_61_cast_fp16 = conv(dilations = var_3357, groups = var_1186, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3355, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_143_cast_fp16)[name = tensor("k_61_cast_fp16")]; + tensor var_3361 = const()[name = tensor("op_3361"), val = tensor([1, 1])]; + tensor var_3363 = const()[name = tensor("op_3363"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389644800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(390873664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_61_cast_fp16 = conv(dilations = var_3363, groups = var_1186, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3361, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_143_cast_fp16)[name = tensor("v_61_cast_fp16")]; + tensor var_3367 = const()[name = tensor("op_3367"), val = tensor([1, 20, 64, -1])]; + tensor var_3368_cast_fp16 = reshape(shape = var_3367, x = q_61_cast_fp16)[name = tensor("op_3368_cast_fp16")]; + tensor var_3369 = const()[name = tensor("op_3369"), val = tensor([1, 20, 64, -1])]; + tensor var_3370_cast_fp16 = reshape(shape = var_3369, x = k_61_cast_fp16)[name = tensor("op_3370_cast_fp16")]; + tensor var_3371 = const()[name = tensor("op_3371"), val = tensor([1, 20, 64, -1])]; + tensor var_3372_cast_fp16 = reshape(shape = var_3371, x = v_61_cast_fp16)[name = tensor("op_3372_cast_fp16")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3368_cast_fp16, y = var_3370_cast_fp16)[name = tensor("attn_weights_121_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_3376_cast_fp16 = softmax(axis = var_1170, x = attn_weights_123_cast_fp16)[name = tensor("op_3376_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3372_cast_fp16, y = var_3376_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_3380 = const()[name = tensor("op_3380"), val = tensor([1, 1280, 1, -1])]; + tensor input_237_cast_fp16 = reshape(shape = var_3380, x = attn_61_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor var_3385 = const()[name = tensor("op_3385"), val = tensor([1, 1])]; + tensor var_3387 = const()[name = tensor("op_3387"), val = tensor([1, 1])]; + tensor var_3389_pad_type_0 = const()[name = tensor("op_3389_pad_type_0"), val = tensor("custom")]; + tensor var_3389_pad_0 = const()[name = tensor("op_3389_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(390873856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392102720))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392102912)))]; + tensor var_3389_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3387, groups = var_1186, pad = var_3389_pad_0, pad_type = var_3389_pad_type_0, strides = var_3385, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = tensor("op_3389_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = var_3389_cast_fp16, y = inputs_91_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor hidden_states_145_axes_0 = const()[name = tensor("hidden_states_145_axes_0"), val = tensor([1])]; + tensor hidden_states_145_gamma_0_to_fp16 = const()[name = tensor("hidden_states_145_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392105536)))]; + tensor hidden_states_145_beta_0_to_fp16 = const()[name = tensor("hidden_states_145_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392108160)))]; + tensor var_3399_to_fp16 = const()[name = tensor("op_3399_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_145_cast_fp16 = layer_norm(axes = hidden_states_145_axes_0, beta = hidden_states_145_beta_0_to_fp16, epsilon = var_3399_to_fp16, gamma = hidden_states_145_gamma_0_to_fp16, x = inputs_93_cast_fp16)[name = tensor("hidden_states_145_cast_fp16")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor q_63_pad_type_0 = const()[name = tensor("q_63_pad_type_0"), val = tensor("custom")]; + tensor q_63_pad_0 = const()[name = tensor("q_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(392110784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393339648))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_63_cast_fp16 = conv(dilations = var_3416, groups = var_1186, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3414, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_145_cast_fp16)[name = tensor("q_63_cast_fp16")]; + tensor var_3420 = const()[name = tensor("op_3420"), val = tensor([1, 1])]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor k_63_pad_type_0 = const()[name = tensor("k_63_pad_type_0"), val = tensor("custom")]; + tensor k_63_pad_0 = const()[name = tensor("k_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393339840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395305984))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_63_cast_fp16 = conv(dilations = var_3422, groups = var_1186, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3420, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_63_cast_fp16")]; + tensor var_3426 = const()[name = tensor("op_3426"), val = tensor([1, 1])]; + tensor var_3428 = const()[name = tensor("op_3428"), val = tensor([1, 1])]; + tensor v_63_pad_type_0 = const()[name = tensor("v_63_pad_type_0"), val = tensor("custom")]; + tensor v_63_pad_0 = const()[name = tensor("v_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395306176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397272320))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_63_cast_fp16 = conv(dilations = var_3428, groups = var_1186, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3426, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_63_cast_fp16")]; + tensor var_3432 = const()[name = tensor("op_3432"), val = tensor([1, 20, 64, -1])]; + tensor var_3433_cast_fp16 = reshape(shape = var_3432, x = q_63_cast_fp16)[name = tensor("op_3433_cast_fp16")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([1, 20, 64, -1])]; + tensor var_3435_cast_fp16 = reshape(shape = var_3434, x = k_63_cast_fp16)[name = tensor("op_3435_cast_fp16")]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 20, 64, -1])]; + tensor var_3437_cast_fp16 = reshape(shape = var_3436, x = v_63_cast_fp16)[name = tensor("op_3437_cast_fp16")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast_fp16 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3433_cast_fp16, y = var_3435_cast_fp16)[name = tensor("attn_weights_125_cast_fp16")]; + tensor attn_weights_127_cast_fp16 = mul(x = attn_weights_125_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_127_cast_fp16")]; + tensor var_3441_cast_fp16 = softmax(axis = var_1170, x = attn_weights_127_cast_fp16)[name = tensor("op_3441_cast_fp16")]; + tensor attn_63_transpose_x_0 = const()[name = tensor("attn_63_transpose_x_0"), val = tensor(false)]; + tensor attn_63_transpose_y_0 = const()[name = tensor("attn_63_transpose_y_0"), val = tensor(true)]; + tensor attn_63_cast_fp16 = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3437_cast_fp16, y = var_3441_cast_fp16)[name = tensor("attn_63_cast_fp16")]; + tensor var_3445 = const()[name = tensor("op_3445"), val = tensor([1, 1280, 1, -1])]; + tensor input_239_cast_fp16 = reshape(shape = var_3445, x = attn_63_cast_fp16)[name = tensor("input_239_cast_fp16")]; + tensor var_3450 = const()[name = tensor("op_3450"), val = tensor([1, 1])]; + tensor var_3452 = const()[name = tensor("op_3452"), val = tensor([1, 1])]; + tensor var_3454_pad_type_0 = const()[name = tensor("op_3454_pad_type_0"), val = tensor("custom")]; + tensor var_3454_pad_0 = const()[name = tensor("op_3454_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(397272512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398501376))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398501568)))]; + tensor var_3454_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3452, groups = var_1186, pad = var_3454_pad_0, pad_type = var_3454_pad_type_0, strides = var_3450, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_239_cast_fp16)[name = tensor("op_3454_cast_fp16")]; + tensor inputs_95_cast_fp16 = add(x = var_3454_cast_fp16, y = inputs_93_cast_fp16)[name = tensor("inputs_95_cast_fp16")]; + tensor input_241_axes_0 = const()[name = tensor("input_241_axes_0"), val = tensor([1])]; + tensor input_241_gamma_0_to_fp16 = const()[name = tensor("input_241_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398504192)))]; + tensor input_241_beta_0_to_fp16 = const()[name = tensor("input_241_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398506816)))]; + tensor var_3464_to_fp16 = const()[name = tensor("op_3464_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_241_cast_fp16 = layer_norm(axes = input_241_axes_0, beta = input_241_beta_0_to_fp16, epsilon = var_3464_to_fp16, gamma = input_241_gamma_0_to_fp16, x = inputs_95_cast_fp16)[name = tensor("input_241_cast_fp16")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1, 1])]; + tensor var_3482 = const()[name = tensor("op_3482"), val = tensor([1, 1])]; + tensor var_3484_pad_type_0 = const()[name = tensor("op_3484_pad_type_0"), val = tensor("custom")]; + tensor var_3484_pad_0 = const()[name = tensor("op_3484_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(398509440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408339904))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408340096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408347840))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3484_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3482, groups = var_1186, pad = var_3484_pad_0, pad_type = var_3484_pad_type_0, strides = var_3480, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = tensor("op_3484_cast_fp16")]; + tensor var_3485_split_sizes_0 = const()[name = tensor("op_3485_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3485_axis_0 = const()[name = tensor("op_3485_axis_0"), val = tensor(1)]; + tensor var_3485_cast_fp16_0, tensor var_3485_cast_fp16_1 = split(axis = var_3485_axis_0, split_sizes = var_3485_split_sizes_0, x = var_3484_cast_fp16)[name = tensor("op_3485_cast_fp16")]; + tensor var_3487_mode_0 = const()[name = tensor("op_3487_mode_0"), val = tensor("EXACT")]; + tensor var_3487_cast_fp16 = gelu(mode = var_3487_mode_0, x = var_3485_cast_fp16_1)[name = tensor("op_3487_cast_fp16")]; + tensor input_243_cast_fp16 = mul(x = var_3485_cast_fp16_0, y = var_3487_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor var_3491 = const()[name = tensor("op_3491"), val = tensor([1, 1])]; + tensor var_3493 = const()[name = tensor("op_3493"), val = tensor([1, 1])]; + tensor var_3495_pad_type_0 = const()[name = tensor("op_3495_pad_type_0"), val = tensor("custom")]; + tensor var_3495_pad_0 = const()[name = tensor("op_3495_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408348032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413263296))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413263488)))]; + tensor var_3495_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_3493, groups = var_1186, pad = var_3495_pad_0, pad_type = var_3495_pad_type_0, strides = var_3491, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_243_cast_fp16)[name = tensor("op_3495_cast_fp16")]; + tensor inputs_97_cast_fp16 = add(x = var_3495_cast_fp16, y = inputs_95_cast_fp16)[name = tensor("inputs_97_cast_fp16")]; + tensor hidden_states_149_axes_0 = const()[name = tensor("hidden_states_149_axes_0"), val = tensor([1])]; + tensor hidden_states_149_gamma_0_to_fp16 = const()[name = tensor("hidden_states_149_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413266112)))]; + tensor hidden_states_149_beta_0_to_fp16 = const()[name = tensor("hidden_states_149_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413268736)))]; + tensor var_3511_to_fp16 = const()[name = tensor("op_3511_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_149_cast_fp16 = layer_norm(axes = hidden_states_149_axes_0, beta = hidden_states_149_beta_0_to_fp16, epsilon = var_3511_to_fp16, gamma = hidden_states_149_gamma_0_to_fp16, x = inputs_97_cast_fp16)[name = tensor("hidden_states_149_cast_fp16")]; + tensor var_3526 = const()[name = tensor("op_3526"), val = tensor([1, 1])]; + tensor var_3528 = const()[name = tensor("op_3528"), val = tensor([1, 1])]; + tensor q_65_pad_type_0 = const()[name = tensor("q_65_pad_type_0"), val = tensor("custom")]; + tensor q_65_pad_0 = const()[name = tensor("q_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413271360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414500224))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_65_cast_fp16 = conv(dilations = var_3528, groups = var_1186, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3526, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_149_cast_fp16)[name = tensor("q_65_cast_fp16")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([1, 1])]; + tensor var_3534 = const()[name = tensor("op_3534"), val = tensor([1, 1])]; + tensor k_65_pad_type_0 = const()[name = tensor("k_65_pad_type_0"), val = tensor("custom")]; + tensor k_65_pad_0 = const()[name = tensor("k_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(414500416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415729280))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_65_cast_fp16 = conv(dilations = var_3534, groups = var_1186, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3532, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_149_cast_fp16)[name = tensor("k_65_cast_fp16")]; + tensor var_3538 = const()[name = tensor("op_3538"), val = tensor([1, 1])]; + tensor var_3540 = const()[name = tensor("op_3540"), val = tensor([1, 1])]; + tensor v_65_pad_type_0 = const()[name = tensor("v_65_pad_type_0"), val = tensor("custom")]; + tensor v_65_pad_0 = const()[name = tensor("v_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415729472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416958336))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_65_cast_fp16 = conv(dilations = var_3540, groups = var_1186, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3538, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_149_cast_fp16)[name = tensor("v_65_cast_fp16")]; + tensor var_3544 = const()[name = tensor("op_3544"), val = tensor([1, 20, 64, -1])]; + tensor var_3545_cast_fp16 = reshape(shape = var_3544, x = q_65_cast_fp16)[name = tensor("op_3545_cast_fp16")]; + tensor var_3546 = const()[name = tensor("op_3546"), val = tensor([1, 20, 64, -1])]; + tensor var_3547_cast_fp16 = reshape(shape = var_3546, x = k_65_cast_fp16)[name = tensor("op_3547_cast_fp16")]; + tensor var_3548 = const()[name = tensor("op_3548"), val = tensor([1, 20, 64, -1])]; + tensor var_3549_cast_fp16 = reshape(shape = var_3548, x = v_65_cast_fp16)[name = tensor("op_3549_cast_fp16")]; + tensor attn_weights_129_transpose_x_0 = const()[name = tensor("attn_weights_129_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_129_transpose_y_0 = const()[name = tensor("attn_weights_129_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3545_cast_fp16, y = var_3547_cast_fp16)[name = tensor("attn_weights_129_cast_fp16")]; + tensor attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_131_cast_fp16")]; + tensor var_3553_cast_fp16 = softmax(axis = var_1170, x = attn_weights_131_cast_fp16)[name = tensor("op_3553_cast_fp16")]; + tensor attn_65_transpose_x_0 = const()[name = tensor("attn_65_transpose_x_0"), val = tensor(false)]; + tensor attn_65_transpose_y_0 = const()[name = tensor("attn_65_transpose_y_0"), val = tensor(true)]; + tensor attn_65_cast_fp16 = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3549_cast_fp16, y = var_3553_cast_fp16)[name = tensor("attn_65_cast_fp16")]; + tensor var_3557 = const()[name = tensor("op_3557"), val = tensor([1, 1280, 1, -1])]; + tensor input_245_cast_fp16 = reshape(shape = var_3557, x = attn_65_cast_fp16)[name = tensor("input_245_cast_fp16")]; + tensor var_3562 = const()[name = tensor("op_3562"), val = tensor([1, 1])]; + tensor var_3564 = const()[name = tensor("op_3564"), val = tensor([1, 1])]; + tensor var_3566_pad_type_0 = const()[name = tensor("op_3566_pad_type_0"), val = tensor("custom")]; + tensor var_3566_pad_0 = const()[name = tensor("op_3566_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416958528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418187392))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418187584)))]; + tensor var_3566_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_3564, groups = var_1186, pad = var_3566_pad_0, pad_type = var_3566_pad_type_0, strides = var_3562, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_245_cast_fp16)[name = tensor("op_3566_cast_fp16")]; + tensor inputs_99_cast_fp16 = add(x = var_3566_cast_fp16, y = inputs_97_cast_fp16)[name = tensor("inputs_99_cast_fp16")]; + tensor hidden_states_151_axes_0 = const()[name = tensor("hidden_states_151_axes_0"), val = tensor([1])]; + tensor hidden_states_151_gamma_0_to_fp16 = const()[name = tensor("hidden_states_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418190208)))]; + tensor hidden_states_151_beta_0_to_fp16 = const()[name = tensor("hidden_states_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418192832)))]; + tensor var_3576_to_fp16 = const()[name = tensor("op_3576_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_151_cast_fp16 = layer_norm(axes = hidden_states_151_axes_0, beta = hidden_states_151_beta_0_to_fp16, epsilon = var_3576_to_fp16, gamma = hidden_states_151_gamma_0_to_fp16, x = inputs_99_cast_fp16)[name = tensor("hidden_states_151_cast_fp16")]; + tensor var_3591 = const()[name = tensor("op_3591"), val = tensor([1, 1])]; + tensor var_3593 = const()[name = tensor("op_3593"), val = tensor([1, 1])]; + tensor q_67_pad_type_0 = const()[name = tensor("q_67_pad_type_0"), val = tensor("custom")]; + tensor q_67_pad_0 = const()[name = tensor("q_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418195456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419424320))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_67_cast_fp16 = conv(dilations = var_3593, groups = var_1186, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3591, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_151_cast_fp16)[name = tensor("q_67_cast_fp16")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([1, 1])]; + tensor var_3599 = const()[name = tensor("op_3599"), val = tensor([1, 1])]; + tensor k_67_pad_type_0 = const()[name = tensor("k_67_pad_type_0"), val = tensor("custom")]; + tensor k_67_pad_0 = const()[name = tensor("k_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419424512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421390656))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_67_cast_fp16 = conv(dilations = var_3599, groups = var_1186, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3597, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_67_cast_fp16")]; + tensor var_3603 = const()[name = tensor("op_3603"), val = tensor([1, 1])]; + tensor var_3605 = const()[name = tensor("op_3605"), val = tensor([1, 1])]; + tensor v_67_pad_type_0 = const()[name = tensor("v_67_pad_type_0"), val = tensor("custom")]; + tensor v_67_pad_0 = const()[name = tensor("v_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421390848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423356992))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_67_cast_fp16 = conv(dilations = var_3605, groups = var_1186, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3603, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_67_cast_fp16")]; + tensor var_3609 = const()[name = tensor("op_3609"), val = tensor([1, 20, 64, -1])]; + tensor var_3610_cast_fp16 = reshape(shape = var_3609, x = q_67_cast_fp16)[name = tensor("op_3610_cast_fp16")]; + tensor var_3611 = const()[name = tensor("op_3611"), val = tensor([1, 20, 64, -1])]; + tensor var_3612_cast_fp16 = reshape(shape = var_3611, x = k_67_cast_fp16)[name = tensor("op_3612_cast_fp16")]; + tensor var_3613 = const()[name = tensor("op_3613"), val = tensor([1, 20, 64, -1])]; + tensor var_3614_cast_fp16 = reshape(shape = var_3613, x = v_67_cast_fp16)[name = tensor("op_3614_cast_fp16")]; + tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_133_cast_fp16 = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3610_cast_fp16, y = var_3612_cast_fp16)[name = tensor("attn_weights_133_cast_fp16")]; + tensor attn_weights_135_cast_fp16 = mul(x = attn_weights_133_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_135_cast_fp16")]; + tensor var_3618_cast_fp16 = softmax(axis = var_1170, x = attn_weights_135_cast_fp16)[name = tensor("op_3618_cast_fp16")]; + tensor attn_67_transpose_x_0 = const()[name = tensor("attn_67_transpose_x_0"), val = tensor(false)]; + tensor attn_67_transpose_y_0 = const()[name = tensor("attn_67_transpose_y_0"), val = tensor(true)]; + tensor attn_67_cast_fp16 = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3614_cast_fp16, y = var_3618_cast_fp16)[name = tensor("attn_67_cast_fp16")]; + tensor var_3622 = const()[name = tensor("op_3622"), val = tensor([1, 1280, 1, -1])]; + tensor input_247_cast_fp16 = reshape(shape = var_3622, x = attn_67_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor var_3627 = const()[name = tensor("op_3627"), val = tensor([1, 1])]; + tensor var_3629 = const()[name = tensor("op_3629"), val = tensor([1, 1])]; + tensor var_3631_pad_type_0 = const()[name = tensor("op_3631_pad_type_0"), val = tensor("custom")]; + tensor var_3631_pad_0 = const()[name = tensor("op_3631_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423357184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424586048))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424586240)))]; + tensor var_3631_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_3629, groups = var_1186, pad = var_3631_pad_0, pad_type = var_3631_pad_type_0, strides = var_3627, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = tensor("op_3631_cast_fp16")]; + tensor inputs_101_cast_fp16 = add(x = var_3631_cast_fp16, y = inputs_99_cast_fp16)[name = tensor("inputs_101_cast_fp16")]; + tensor input_249_axes_0 = const()[name = tensor("input_249_axes_0"), val = tensor([1])]; + tensor input_249_gamma_0_to_fp16 = const()[name = tensor("input_249_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424588864)))]; + tensor input_249_beta_0_to_fp16 = const()[name = tensor("input_249_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424591488)))]; + tensor var_3641_to_fp16 = const()[name = tensor("op_3641_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_249_cast_fp16 = layer_norm(axes = input_249_axes_0, beta = input_249_beta_0_to_fp16, epsilon = var_3641_to_fp16, gamma = input_249_gamma_0_to_fp16, x = inputs_101_cast_fp16)[name = tensor("input_249_cast_fp16")]; + tensor var_3657 = const()[name = tensor("op_3657"), val = tensor([1, 1])]; + tensor var_3659 = const()[name = tensor("op_3659"), val = tensor([1, 1])]; + tensor var_3661_pad_type_0 = const()[name = tensor("op_3661_pad_type_0"), val = tensor("custom")]; + tensor var_3661_pad_0 = const()[name = tensor("op_3661_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424594112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434424576))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434424768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434432512))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3661_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3659, groups = var_1186, pad = var_3661_pad_0, pad_type = var_3661_pad_type_0, strides = var_3657, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_249_cast_fp16)[name = tensor("op_3661_cast_fp16")]; + tensor var_3662_split_sizes_0 = const()[name = tensor("op_3662_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3662_axis_0 = const()[name = tensor("op_3662_axis_0"), val = tensor(1)]; + tensor var_3662_cast_fp16_0, tensor var_3662_cast_fp16_1 = split(axis = var_3662_axis_0, split_sizes = var_3662_split_sizes_0, x = var_3661_cast_fp16)[name = tensor("op_3662_cast_fp16")]; + tensor var_3664_mode_0 = const()[name = tensor("op_3664_mode_0"), val = tensor("EXACT")]; + tensor var_3664_cast_fp16 = gelu(mode = var_3664_mode_0, x = var_3662_cast_fp16_1)[name = tensor("op_3664_cast_fp16")]; + tensor input_251_cast_fp16 = mul(x = var_3662_cast_fp16_0, y = var_3664_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor var_3668 = const()[name = tensor("op_3668"), val = tensor([1, 1])]; + tensor var_3670 = const()[name = tensor("op_3670"), val = tensor([1, 1])]; + tensor var_3672_pad_type_0 = const()[name = tensor("op_3672_pad_type_0"), val = tensor("custom")]; + tensor var_3672_pad_0 = const()[name = tensor("op_3672_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(434432704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439347968))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439348160)))]; + tensor var_3672_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_3670, groups = var_1186, pad = var_3672_pad_0, pad_type = var_3672_pad_type_0, strides = var_3668, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = tensor("op_3672_cast_fp16")]; + tensor inputs_103_cast_fp16 = add(x = var_3672_cast_fp16, y = inputs_101_cast_fp16)[name = tensor("inputs_103_cast_fp16")]; + tensor hidden_states_155_axes_0 = const()[name = tensor("hidden_states_155_axes_0"), val = tensor([1])]; + tensor hidden_states_155_gamma_0_to_fp16 = const()[name = tensor("hidden_states_155_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439350784)))]; + tensor hidden_states_155_beta_0_to_fp16 = const()[name = tensor("hidden_states_155_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439353408)))]; + tensor var_3688_to_fp16 = const()[name = tensor("op_3688_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_155_cast_fp16 = layer_norm(axes = hidden_states_155_axes_0, beta = hidden_states_155_beta_0_to_fp16, epsilon = var_3688_to_fp16, gamma = hidden_states_155_gamma_0_to_fp16, x = inputs_103_cast_fp16)[name = tensor("hidden_states_155_cast_fp16")]; + tensor var_3703 = const()[name = tensor("op_3703"), val = tensor([1, 1])]; + tensor var_3705 = const()[name = tensor("op_3705"), val = tensor([1, 1])]; + tensor q_69_pad_type_0 = const()[name = tensor("q_69_pad_type_0"), val = tensor("custom")]; + tensor q_69_pad_0 = const()[name = tensor("q_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(439356032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440584896))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_69_cast_fp16 = conv(dilations = var_3705, groups = var_1186, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3703, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_155_cast_fp16)[name = tensor("q_69_cast_fp16")]; + tensor var_3709 = const()[name = tensor("op_3709"), val = tensor([1, 1])]; + tensor var_3711 = const()[name = tensor("op_3711"), val = tensor([1, 1])]; + tensor k_69_pad_type_0 = const()[name = tensor("k_69_pad_type_0"), val = tensor("custom")]; + tensor k_69_pad_0 = const()[name = tensor("k_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440585088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441813952))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_69_cast_fp16 = conv(dilations = var_3711, groups = var_1186, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3709, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_155_cast_fp16)[name = tensor("k_69_cast_fp16")]; + tensor var_3715 = const()[name = tensor("op_3715"), val = tensor([1, 1])]; + tensor var_3717 = const()[name = tensor("op_3717"), val = tensor([1, 1])]; + tensor v_69_pad_type_0 = const()[name = tensor("v_69_pad_type_0"), val = tensor("custom")]; + tensor v_69_pad_0 = const()[name = tensor("v_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441814144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(443043008))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_69_cast_fp16 = conv(dilations = var_3717, groups = var_1186, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3715, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_155_cast_fp16)[name = tensor("v_69_cast_fp16")]; + tensor var_3721 = const()[name = tensor("op_3721"), val = tensor([1, 20, 64, -1])]; + tensor var_3722_cast_fp16 = reshape(shape = var_3721, x = q_69_cast_fp16)[name = tensor("op_3722_cast_fp16")]; + tensor var_3723 = const()[name = tensor("op_3723"), val = tensor([1, 20, 64, -1])]; + tensor var_3724_cast_fp16 = reshape(shape = var_3723, x = k_69_cast_fp16)[name = tensor("op_3724_cast_fp16")]; + tensor var_3725 = const()[name = tensor("op_3725"), val = tensor([1, 20, 64, -1])]; + tensor var_3726_cast_fp16 = reshape(shape = var_3725, x = v_69_cast_fp16)[name = tensor("op_3726_cast_fp16")]; + tensor attn_weights_137_transpose_x_0 = const()[name = tensor("attn_weights_137_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_137_transpose_y_0 = const()[name = tensor("attn_weights_137_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3722_cast_fp16, y = var_3724_cast_fp16)[name = tensor("attn_weights_137_cast_fp16")]; + tensor attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_139_cast_fp16")]; + tensor var_3730_cast_fp16 = softmax(axis = var_1170, x = attn_weights_139_cast_fp16)[name = tensor("op_3730_cast_fp16")]; + tensor attn_69_transpose_x_0 = const()[name = tensor("attn_69_transpose_x_0"), val = tensor(false)]; + tensor attn_69_transpose_y_0 = const()[name = tensor("attn_69_transpose_y_0"), val = tensor(true)]; + tensor attn_69_cast_fp16 = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3726_cast_fp16, y = var_3730_cast_fp16)[name = tensor("attn_69_cast_fp16")]; + tensor var_3734 = const()[name = tensor("op_3734"), val = tensor([1, 1280, 1, -1])]; + tensor input_253_cast_fp16 = reshape(shape = var_3734, x = attn_69_cast_fp16)[name = tensor("input_253_cast_fp16")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1, 1])]; + tensor var_3741 = const()[name = tensor("op_3741"), val = tensor([1, 1])]; + tensor var_3743_pad_type_0 = const()[name = tensor("op_3743_pad_type_0"), val = tensor("custom")]; + tensor var_3743_pad_0 = const()[name = tensor("op_3743_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(443043200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444272064))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444272256)))]; + tensor var_3743_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_3741, groups = var_1186, pad = var_3743_pad_0, pad_type = var_3743_pad_type_0, strides = var_3739, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_253_cast_fp16)[name = tensor("op_3743_cast_fp16")]; + tensor inputs_105_cast_fp16 = add(x = var_3743_cast_fp16, y = inputs_103_cast_fp16)[name = tensor("inputs_105_cast_fp16")]; + tensor hidden_states_157_axes_0 = const()[name = tensor("hidden_states_157_axes_0"), val = tensor([1])]; + tensor hidden_states_157_gamma_0_to_fp16 = const()[name = tensor("hidden_states_157_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444274880)))]; + tensor hidden_states_157_beta_0_to_fp16 = const()[name = tensor("hidden_states_157_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444277504)))]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_157_cast_fp16 = layer_norm(axes = hidden_states_157_axes_0, beta = hidden_states_157_beta_0_to_fp16, epsilon = var_3753_to_fp16, gamma = hidden_states_157_gamma_0_to_fp16, x = inputs_105_cast_fp16)[name = tensor("hidden_states_157_cast_fp16")]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor var_3770 = const()[name = tensor("op_3770"), val = tensor([1, 1])]; + tensor q_71_pad_type_0 = const()[name = tensor("q_71_pad_type_0"), val = tensor("custom")]; + tensor q_71_pad_0 = const()[name = tensor("q_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444280128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445508992))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_71_cast_fp16 = conv(dilations = var_3770, groups = var_1186, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3768, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_157_cast_fp16)[name = tensor("q_71_cast_fp16")]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor var_3776 = const()[name = tensor("op_3776"), val = tensor([1, 1])]; + tensor k_71_pad_type_0 = const()[name = tensor("k_71_pad_type_0"), val = tensor("custom")]; + tensor k_71_pad_0 = const()[name = tensor("k_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445509184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(447475328))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_71_cast_fp16 = conv(dilations = var_3776, groups = var_1186, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3774, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_71_cast_fp16")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([1, 1])]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([1, 1])]; + tensor v_71_pad_type_0 = const()[name = tensor("v_71_pad_type_0"), val = tensor("custom")]; + tensor v_71_pad_0 = const()[name = tensor("v_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(447475520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(449441664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_71_cast_fp16 = conv(dilations = var_3782, groups = var_1186, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3780, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_71_cast_fp16")]; + tensor var_3786 = const()[name = tensor("op_3786"), val = tensor([1, 20, 64, -1])]; + tensor var_3787_cast_fp16 = reshape(shape = var_3786, x = q_71_cast_fp16)[name = tensor("op_3787_cast_fp16")]; + tensor var_3788 = const()[name = tensor("op_3788"), val = tensor([1, 20, 64, -1])]; + tensor var_3789_cast_fp16 = reshape(shape = var_3788, x = k_71_cast_fp16)[name = tensor("op_3789_cast_fp16")]; + tensor var_3790 = const()[name = tensor("op_3790"), val = tensor([1, 20, 64, -1])]; + tensor var_3791_cast_fp16 = reshape(shape = var_3790, x = v_71_cast_fp16)[name = tensor("op_3791_cast_fp16")]; + tensor attn_weights_141_transpose_x_0 = const()[name = tensor("attn_weights_141_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_141_transpose_y_0 = const()[name = tensor("attn_weights_141_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_141_cast_fp16 = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3787_cast_fp16, y = var_3789_cast_fp16)[name = tensor("attn_weights_141_cast_fp16")]; + tensor attn_weights_143_cast_fp16 = mul(x = attn_weights_141_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_143_cast_fp16")]; + tensor var_3795_cast_fp16 = softmax(axis = var_1170, x = attn_weights_143_cast_fp16)[name = tensor("op_3795_cast_fp16")]; + tensor attn_71_transpose_x_0 = const()[name = tensor("attn_71_transpose_x_0"), val = tensor(false)]; + tensor attn_71_transpose_y_0 = const()[name = tensor("attn_71_transpose_y_0"), val = tensor(true)]; + tensor attn_71_cast_fp16 = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3791_cast_fp16, y = var_3795_cast_fp16)[name = tensor("attn_71_cast_fp16")]; + tensor var_3799 = const()[name = tensor("op_3799"), val = tensor([1, 1280, 1, -1])]; + tensor input_255_cast_fp16 = reshape(shape = var_3799, x = attn_71_cast_fp16)[name = tensor("input_255_cast_fp16")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1, 1])]; + tensor var_3806 = const()[name = tensor("op_3806"), val = tensor([1, 1])]; + tensor var_3808_pad_type_0 = const()[name = tensor("op_3808_pad_type_0"), val = tensor("custom")]; + tensor var_3808_pad_0 = const()[name = tensor("op_3808_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(449441856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450670720))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450670912)))]; + tensor var_3808_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_3806, groups = var_1186, pad = var_3808_pad_0, pad_type = var_3808_pad_type_0, strides = var_3804, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = tensor("op_3808_cast_fp16")]; + tensor inputs_107_cast_fp16 = add(x = var_3808_cast_fp16, y = inputs_105_cast_fp16)[name = tensor("inputs_107_cast_fp16")]; + tensor input_257_axes_0 = const()[name = tensor("input_257_axes_0"), val = tensor([1])]; + tensor input_257_gamma_0_to_fp16 = const()[name = tensor("input_257_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450673536)))]; + tensor input_257_beta_0_to_fp16 = const()[name = tensor("input_257_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450676160)))]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_257_cast_fp16 = layer_norm(axes = input_257_axes_0, beta = input_257_beta_0_to_fp16, epsilon = var_3818_to_fp16, gamma = input_257_gamma_0_to_fp16, x = inputs_107_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor var_3834 = const()[name = tensor("op_3834"), val = tensor([1, 1])]; + tensor var_3836 = const()[name = tensor("op_3836"), val = tensor([1, 1])]; + tensor var_3838_pad_type_0 = const()[name = tensor("op_3838_pad_type_0"), val = tensor("custom")]; + tensor var_3838_pad_0 = const()[name = tensor("op_3838_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(450678784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460509248))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460509440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460517184))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3838_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3836, groups = var_1186, pad = var_3838_pad_0, pad_type = var_3838_pad_type_0, strides = var_3834, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = tensor("op_3838_cast_fp16")]; + tensor var_3839_split_sizes_0 = const()[name = tensor("op_3839_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3839_axis_0 = const()[name = tensor("op_3839_axis_0"), val = tensor(1)]; + tensor var_3839_cast_fp16_0, tensor var_3839_cast_fp16_1 = split(axis = var_3839_axis_0, split_sizes = var_3839_split_sizes_0, x = var_3838_cast_fp16)[name = tensor("op_3839_cast_fp16")]; + tensor var_3841_mode_0 = const()[name = tensor("op_3841_mode_0"), val = tensor("EXACT")]; + tensor var_3841_cast_fp16 = gelu(mode = var_3841_mode_0, x = var_3839_cast_fp16_1)[name = tensor("op_3841_cast_fp16")]; + tensor input_259_cast_fp16 = mul(x = var_3839_cast_fp16_0, y = var_3841_cast_fp16)[name = tensor("input_259_cast_fp16")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([1, 1])]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([1, 1])]; + tensor var_3849_pad_type_0 = const()[name = tensor("op_3849_pad_type_0"), val = tensor("custom")]; + tensor var_3849_pad_0 = const()[name = tensor("op_3849_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460517376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465432640))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465432832)))]; + tensor var_3849_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_3847, groups = var_1186, pad = var_3849_pad_0, pad_type = var_3849_pad_type_0, strides = var_3845, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = tensor("op_3849_cast_fp16")]; + tensor inputs_109_cast_fp16 = add(x = var_3849_cast_fp16, y = inputs_107_cast_fp16)[name = tensor("inputs_109_cast_fp16")]; + tensor hidden_states_161_axes_0 = const()[name = tensor("hidden_states_161_axes_0"), val = tensor([1])]; + tensor hidden_states_161_gamma_0_to_fp16 = const()[name = tensor("hidden_states_161_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465435456)))]; + tensor hidden_states_161_beta_0_to_fp16 = const()[name = tensor("hidden_states_161_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465438080)))]; + tensor var_3865_to_fp16 = const()[name = tensor("op_3865_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_161_cast_fp16 = layer_norm(axes = hidden_states_161_axes_0, beta = hidden_states_161_beta_0_to_fp16, epsilon = var_3865_to_fp16, gamma = hidden_states_161_gamma_0_to_fp16, x = inputs_109_cast_fp16)[name = tensor("hidden_states_161_cast_fp16")]; + tensor var_3880 = const()[name = tensor("op_3880"), val = tensor([1, 1])]; + tensor var_3882 = const()[name = tensor("op_3882"), val = tensor([1, 1])]; + tensor q_73_pad_type_0 = const()[name = tensor("q_73_pad_type_0"), val = tensor("custom")]; + tensor q_73_pad_0 = const()[name = tensor("q_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(465440704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466669568))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_73_cast_fp16 = conv(dilations = var_3882, groups = var_1186, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_3880, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = tensor("q_73_cast_fp16")]; + tensor var_3886 = const()[name = tensor("op_3886"), val = tensor([1, 1])]; + tensor var_3888 = const()[name = tensor("op_3888"), val = tensor([1, 1])]; + tensor k_73_pad_type_0 = const()[name = tensor("k_73_pad_type_0"), val = tensor("custom")]; + tensor k_73_pad_0 = const()[name = tensor("k_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466669760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467898624))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_73_cast_fp16 = conv(dilations = var_3888, groups = var_1186, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_3886, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = tensor("k_73_cast_fp16")]; + tensor var_3892 = const()[name = tensor("op_3892"), val = tensor([1, 1])]; + tensor var_3894 = const()[name = tensor("op_3894"), val = tensor([1, 1])]; + tensor v_73_pad_type_0 = const()[name = tensor("v_73_pad_type_0"), val = tensor("custom")]; + tensor v_73_pad_0 = const()[name = tensor("v_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467898816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469127680))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_73_cast_fp16 = conv(dilations = var_3894, groups = var_1186, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_3892, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_161_cast_fp16)[name = tensor("v_73_cast_fp16")]; + tensor var_3898 = const()[name = tensor("op_3898"), val = tensor([1, 20, 64, -1])]; + tensor var_3899_cast_fp16 = reshape(shape = var_3898, x = q_73_cast_fp16)[name = tensor("op_3899_cast_fp16")]; + tensor var_3900 = const()[name = tensor("op_3900"), val = tensor([1, 20, 64, -1])]; + tensor var_3901_cast_fp16 = reshape(shape = var_3900, x = k_73_cast_fp16)[name = tensor("op_3901_cast_fp16")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 20, 64, -1])]; + tensor var_3903_cast_fp16 = reshape(shape = var_3902, x = v_73_cast_fp16)[name = tensor("op_3903_cast_fp16")]; + tensor attn_weights_145_transpose_x_0 = const()[name = tensor("attn_weights_145_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_145_transpose_y_0 = const()[name = tensor("attn_weights_145_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3899_cast_fp16, y = var_3901_cast_fp16)[name = tensor("attn_weights_145_cast_fp16")]; + tensor attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_147_cast_fp16")]; + tensor var_3907_cast_fp16 = softmax(axis = var_1170, x = attn_weights_147_cast_fp16)[name = tensor("op_3907_cast_fp16")]; + tensor attn_73_transpose_x_0 = const()[name = tensor("attn_73_transpose_x_0"), val = tensor(false)]; + tensor attn_73_transpose_y_0 = const()[name = tensor("attn_73_transpose_y_0"), val = tensor(true)]; + tensor attn_73_cast_fp16 = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_3903_cast_fp16, y = var_3907_cast_fp16)[name = tensor("attn_73_cast_fp16")]; + tensor var_3911 = const()[name = tensor("op_3911"), val = tensor([1, 1280, 1, -1])]; + tensor input_261_cast_fp16 = reshape(shape = var_3911, x = attn_73_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor var_3916 = const()[name = tensor("op_3916"), val = tensor([1, 1])]; + tensor var_3918 = const()[name = tensor("op_3918"), val = tensor([1, 1])]; + tensor var_3920_pad_type_0 = const()[name = tensor("op_3920_pad_type_0"), val = tensor("custom")]; + tensor var_3920_pad_0 = const()[name = tensor("op_3920_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469127872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470356736))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470356928)))]; + tensor var_3920_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_3918, groups = var_1186, pad = var_3920_pad_0, pad_type = var_3920_pad_type_0, strides = var_3916, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_261_cast_fp16)[name = tensor("op_3920_cast_fp16")]; + tensor inputs_111_cast_fp16 = add(x = var_3920_cast_fp16, y = inputs_109_cast_fp16)[name = tensor("inputs_111_cast_fp16")]; + tensor hidden_states_163_axes_0 = const()[name = tensor("hidden_states_163_axes_0"), val = tensor([1])]; + tensor hidden_states_163_gamma_0_to_fp16 = const()[name = tensor("hidden_states_163_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470359552)))]; + tensor hidden_states_163_beta_0_to_fp16 = const()[name = tensor("hidden_states_163_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470362176)))]; + tensor var_3930_to_fp16 = const()[name = tensor("op_3930_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_163_cast_fp16 = layer_norm(axes = hidden_states_163_axes_0, beta = hidden_states_163_beta_0_to_fp16, epsilon = var_3930_to_fp16, gamma = hidden_states_163_gamma_0_to_fp16, x = inputs_111_cast_fp16)[name = tensor("hidden_states_163_cast_fp16")]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor([1, 1])]; + tensor var_3947 = const()[name = tensor("op_3947"), val = tensor([1, 1])]; + tensor q_75_pad_type_0 = const()[name = tensor("q_75_pad_type_0"), val = tensor("custom")]; + tensor q_75_pad_0 = const()[name = tensor("q_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470364800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471593664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_75_cast_fp16 = conv(dilations = var_3947, groups = var_1186, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_3945, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_163_cast_fp16)[name = tensor("q_75_cast_fp16")]; + tensor var_3951 = const()[name = tensor("op_3951"), val = tensor([1, 1])]; + tensor var_3953 = const()[name = tensor("op_3953"), val = tensor([1, 1])]; + tensor k_75_pad_type_0 = const()[name = tensor("k_75_pad_type_0"), val = tensor("custom")]; + tensor k_75_pad_0 = const()[name = tensor("k_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(471593856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473560000))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_75_cast_fp16 = conv(dilations = var_3953, groups = var_1186, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_3951, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_75_cast_fp16")]; + tensor var_3957 = const()[name = tensor("op_3957"), val = tensor([1, 1])]; + tensor var_3959 = const()[name = tensor("op_3959"), val = tensor([1, 1])]; + tensor v_75_pad_type_0 = const()[name = tensor("v_75_pad_type_0"), val = tensor("custom")]; + tensor v_75_pad_0 = const()[name = tensor("v_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473560192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475526336))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_75_cast_fp16 = conv(dilations = var_3959, groups = var_1186, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_3957, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_75_cast_fp16")]; + tensor var_3963 = const()[name = tensor("op_3963"), val = tensor([1, 20, 64, -1])]; + tensor var_3964_cast_fp16 = reshape(shape = var_3963, x = q_75_cast_fp16)[name = tensor("op_3964_cast_fp16")]; + tensor var_3965 = const()[name = tensor("op_3965"), val = tensor([1, 20, 64, -1])]; + tensor var_3966_cast_fp16 = reshape(shape = var_3965, x = k_75_cast_fp16)[name = tensor("op_3966_cast_fp16")]; + tensor var_3967 = const()[name = tensor("op_3967"), val = tensor([1, 20, 64, -1])]; + tensor var_3968_cast_fp16 = reshape(shape = var_3967, x = v_75_cast_fp16)[name = tensor("op_3968_cast_fp16")]; + tensor attn_weights_149_transpose_x_0 = const()[name = tensor("attn_weights_149_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_149_transpose_y_0 = const()[name = tensor("attn_weights_149_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_149_cast_fp16 = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_3964_cast_fp16, y = var_3966_cast_fp16)[name = tensor("attn_weights_149_cast_fp16")]; + tensor attn_weights_151_cast_fp16 = mul(x = attn_weights_149_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_151_cast_fp16")]; + tensor var_3972_cast_fp16 = softmax(axis = var_1170, x = attn_weights_151_cast_fp16)[name = tensor("op_3972_cast_fp16")]; + tensor attn_75_transpose_x_0 = const()[name = tensor("attn_75_transpose_x_0"), val = tensor(false)]; + tensor attn_75_transpose_y_0 = const()[name = tensor("attn_75_transpose_y_0"), val = tensor(true)]; + tensor attn_75_cast_fp16 = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_3968_cast_fp16, y = var_3972_cast_fp16)[name = tensor("attn_75_cast_fp16")]; + tensor var_3976 = const()[name = tensor("op_3976"), val = tensor([1, 1280, 1, -1])]; + tensor input_263_cast_fp16 = reshape(shape = var_3976, x = attn_75_cast_fp16)[name = tensor("input_263_cast_fp16")]; + tensor var_3981 = const()[name = tensor("op_3981"), val = tensor([1, 1])]; + tensor var_3983 = const()[name = tensor("op_3983"), val = tensor([1, 1])]; + tensor var_3985_pad_type_0 = const()[name = tensor("op_3985_pad_type_0"), val = tensor("custom")]; + tensor var_3985_pad_0 = const()[name = tensor("op_3985_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475526528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476755392))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476755584)))]; + tensor var_3985_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_3983, groups = var_1186, pad = var_3985_pad_0, pad_type = var_3985_pad_type_0, strides = var_3981, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_263_cast_fp16)[name = tensor("op_3985_cast_fp16")]; + tensor inputs_113_cast_fp16 = add(x = var_3985_cast_fp16, y = inputs_111_cast_fp16)[name = tensor("inputs_113_cast_fp16")]; + tensor input_265_axes_0 = const()[name = tensor("input_265_axes_0"), val = tensor([1])]; + tensor input_265_gamma_0_to_fp16 = const()[name = tensor("input_265_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476758208)))]; + tensor input_265_beta_0_to_fp16 = const()[name = tensor("input_265_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476760832)))]; + tensor var_3995_to_fp16 = const()[name = tensor("op_3995_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_265_cast_fp16 = layer_norm(axes = input_265_axes_0, beta = input_265_beta_0_to_fp16, epsilon = var_3995_to_fp16, gamma = input_265_gamma_0_to_fp16, x = inputs_113_cast_fp16)[name = tensor("input_265_cast_fp16")]; + tensor var_4011 = const()[name = tensor("op_4011"), val = tensor([1, 1])]; + tensor var_4013 = const()[name = tensor("op_4013"), val = tensor([1, 1])]; + tensor var_4015_pad_type_0 = const()[name = tensor("op_4015_pad_type_0"), val = tensor("custom")]; + tensor var_4015_pad_0 = const()[name = tensor("op_4015_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476763456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486593920))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486594112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486601856))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4015_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4013, groups = var_1186, pad = var_4015_pad_0, pad_type = var_4015_pad_type_0, strides = var_4011, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_265_cast_fp16)[name = tensor("op_4015_cast_fp16")]; + tensor var_4016_split_sizes_0 = const()[name = tensor("op_4016_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4016_axis_0 = const()[name = tensor("op_4016_axis_0"), val = tensor(1)]; + tensor var_4016_cast_fp16_0, tensor var_4016_cast_fp16_1 = split(axis = var_4016_axis_0, split_sizes = var_4016_split_sizes_0, x = var_4015_cast_fp16)[name = tensor("op_4016_cast_fp16")]; + tensor var_4018_mode_0 = const()[name = tensor("op_4018_mode_0"), val = tensor("EXACT")]; + tensor var_4018_cast_fp16 = gelu(mode = var_4018_mode_0, x = var_4016_cast_fp16_1)[name = tensor("op_4018_cast_fp16")]; + tensor input_267_cast_fp16 = mul(x = var_4016_cast_fp16_0, y = var_4018_cast_fp16)[name = tensor("input_267_cast_fp16")]; + tensor var_4022 = const()[name = tensor("op_4022"), val = tensor([1, 1])]; + tensor var_4024 = const()[name = tensor("op_4024"), val = tensor([1, 1])]; + tensor var_4026_pad_type_0 = const()[name = tensor("op_4026_pad_type_0"), val = tensor("custom")]; + tensor var_4026_pad_0 = const()[name = tensor("op_4026_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486602048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491517312))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491517504)))]; + tensor var_4026_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_4024, groups = var_1186, pad = var_4026_pad_0, pad_type = var_4026_pad_type_0, strides = var_4022, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = tensor("op_4026_cast_fp16")]; + tensor inputs_115_cast_fp16 = add(x = var_4026_cast_fp16, y = inputs_113_cast_fp16)[name = tensor("inputs_115_cast_fp16")]; + tensor hidden_states_167_axes_0 = const()[name = tensor("hidden_states_167_axes_0"), val = tensor([1])]; + tensor hidden_states_167_gamma_0_to_fp16 = const()[name = tensor("hidden_states_167_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491520128)))]; + tensor hidden_states_167_beta_0_to_fp16 = const()[name = tensor("hidden_states_167_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491522752)))]; + tensor var_4042_to_fp16 = const()[name = tensor("op_4042_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_167_cast_fp16 = layer_norm(axes = hidden_states_167_axes_0, beta = hidden_states_167_beta_0_to_fp16, epsilon = var_4042_to_fp16, gamma = hidden_states_167_gamma_0_to_fp16, x = inputs_115_cast_fp16)[name = tensor("hidden_states_167_cast_fp16")]; + tensor var_4057 = const()[name = tensor("op_4057"), val = tensor([1, 1])]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor q_77_pad_type_0 = const()[name = tensor("q_77_pad_type_0"), val = tensor("custom")]; + tensor q_77_pad_0 = const()[name = tensor("q_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491525376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492754240))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_77_cast_fp16 = conv(dilations = var_4059, groups = var_1186, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4057, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("q_77_cast_fp16")]; + tensor var_4063 = const()[name = tensor("op_4063"), val = tensor([1, 1])]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor k_77_pad_type_0 = const()[name = tensor("k_77_pad_type_0"), val = tensor("custom")]; + tensor k_77_pad_0 = const()[name = tensor("k_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492754432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493983296))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_77_cast_fp16 = conv(dilations = var_4065, groups = var_1186, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4063, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("k_77_cast_fp16")]; + tensor var_4069 = const()[name = tensor("op_4069"), val = tensor([1, 1])]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor v_77_pad_type_0 = const()[name = tensor("v_77_pad_type_0"), val = tensor("custom")]; + tensor v_77_pad_0 = const()[name = tensor("v_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493983488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495212352))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_77_cast_fp16 = conv(dilations = var_4071, groups = var_1186, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4069, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast_fp16)[name = tensor("v_77_cast_fp16")]; + tensor var_4075 = const()[name = tensor("op_4075"), val = tensor([1, 20, 64, -1])]; + tensor var_4076_cast_fp16 = reshape(shape = var_4075, x = q_77_cast_fp16)[name = tensor("op_4076_cast_fp16")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([1, 20, 64, -1])]; + tensor var_4078_cast_fp16 = reshape(shape = var_4077, x = k_77_cast_fp16)[name = tensor("op_4078_cast_fp16")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([1, 20, 64, -1])]; + tensor var_4080_cast_fp16 = reshape(shape = var_4079, x = v_77_cast_fp16)[name = tensor("op_4080_cast_fp16")]; + tensor attn_weights_153_transpose_x_0 = const()[name = tensor("attn_weights_153_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_153_transpose_y_0 = const()[name = tensor("attn_weights_153_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4076_cast_fp16, y = var_4078_cast_fp16)[name = tensor("attn_weights_153_cast_fp16")]; + tensor attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_155_cast_fp16")]; + tensor var_4084_cast_fp16 = softmax(axis = var_1170, x = attn_weights_155_cast_fp16)[name = tensor("op_4084_cast_fp16")]; + tensor attn_77_transpose_x_0 = const()[name = tensor("attn_77_transpose_x_0"), val = tensor(false)]; + tensor attn_77_transpose_y_0 = const()[name = tensor("attn_77_transpose_y_0"), val = tensor(true)]; + tensor attn_77_cast_fp16 = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4080_cast_fp16, y = var_4084_cast_fp16)[name = tensor("attn_77_cast_fp16")]; + tensor var_4088 = const()[name = tensor("op_4088"), val = tensor([1, 1280, 1, -1])]; + tensor input_269_cast_fp16 = reshape(shape = var_4088, x = attn_77_cast_fp16)[name = tensor("input_269_cast_fp16")]; + tensor var_4093 = const()[name = tensor("op_4093"), val = tensor([1, 1])]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097_pad_type_0 = const()[name = tensor("op_4097_pad_type_0"), val = tensor("custom")]; + tensor var_4097_pad_0 = const()[name = tensor("op_4097_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(495212544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496441408))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496441600)))]; + tensor var_4097_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_4095, groups = var_1186, pad = var_4097_pad_0, pad_type = var_4097_pad_type_0, strides = var_4093, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_269_cast_fp16)[name = tensor("op_4097_cast_fp16")]; + tensor inputs_117_cast_fp16 = add(x = var_4097_cast_fp16, y = inputs_115_cast_fp16)[name = tensor("inputs_117_cast_fp16")]; + tensor hidden_states_169_axes_0 = const()[name = tensor("hidden_states_169_axes_0"), val = tensor([1])]; + tensor hidden_states_169_gamma_0_to_fp16 = const()[name = tensor("hidden_states_169_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496444224)))]; + tensor hidden_states_169_beta_0_to_fp16 = const()[name = tensor("hidden_states_169_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496446848)))]; + tensor var_4107_to_fp16 = const()[name = tensor("op_4107_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_169_cast_fp16 = layer_norm(axes = hidden_states_169_axes_0, beta = hidden_states_169_beta_0_to_fp16, epsilon = var_4107_to_fp16, gamma = hidden_states_169_gamma_0_to_fp16, x = inputs_117_cast_fp16)[name = tensor("hidden_states_169_cast_fp16")]; + tensor var_4122 = const()[name = tensor("op_4122"), val = tensor([1, 1])]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor q_79_pad_type_0 = const()[name = tensor("q_79_pad_type_0"), val = tensor("custom")]; + tensor q_79_pad_0 = const()[name = tensor("q_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496449472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(497678336))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_79_cast_fp16 = conv(dilations = var_4124, groups = var_1186, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4122, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast_fp16)[name = tensor("q_79_cast_fp16")]; + tensor var_4128 = const()[name = tensor("op_4128"), val = tensor([1, 1])]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor k_79_pad_type_0 = const()[name = tensor("k_79_pad_type_0"), val = tensor("custom")]; + tensor k_79_pad_0 = const()[name = tensor("k_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(497678528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(499644672))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_79_cast_fp16 = conv(dilations = var_4130, groups = var_1186, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4128, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_79_cast_fp16")]; + tensor var_4134 = const()[name = tensor("op_4134"), val = tensor([1, 1])]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor v_79_pad_type_0 = const()[name = tensor("v_79_pad_type_0"), val = tensor("custom")]; + tensor v_79_pad_0 = const()[name = tensor("v_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(499644864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(501611008))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_79_cast_fp16 = conv(dilations = var_4136, groups = var_1186, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4134, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_79_cast_fp16")]; + tensor var_4140 = const()[name = tensor("op_4140"), val = tensor([1, 20, 64, -1])]; + tensor var_4141_cast_fp16 = reshape(shape = var_4140, x = q_79_cast_fp16)[name = tensor("op_4141_cast_fp16")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([1, 20, 64, -1])]; + tensor var_4143_cast_fp16 = reshape(shape = var_4142, x = k_79_cast_fp16)[name = tensor("op_4143_cast_fp16")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([1, 20, 64, -1])]; + tensor var_4145_cast_fp16 = reshape(shape = var_4144, x = v_79_cast_fp16)[name = tensor("op_4145_cast_fp16")]; + tensor attn_weights_157_transpose_x_0 = const()[name = tensor("attn_weights_157_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_157_transpose_y_0 = const()[name = tensor("attn_weights_157_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_157_cast_fp16 = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4141_cast_fp16, y = var_4143_cast_fp16)[name = tensor("attn_weights_157_cast_fp16")]; + tensor attn_weights_159_cast_fp16 = mul(x = attn_weights_157_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_159_cast_fp16")]; + tensor var_4149_cast_fp16 = softmax(axis = var_1170, x = attn_weights_159_cast_fp16)[name = tensor("op_4149_cast_fp16")]; + tensor attn_79_transpose_x_0 = const()[name = tensor("attn_79_transpose_x_0"), val = tensor(false)]; + tensor attn_79_transpose_y_0 = const()[name = tensor("attn_79_transpose_y_0"), val = tensor(true)]; + tensor attn_79_cast_fp16 = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4145_cast_fp16, y = var_4149_cast_fp16)[name = tensor("attn_79_cast_fp16")]; + tensor var_4153 = const()[name = tensor("op_4153"), val = tensor([1, 1280, 1, -1])]; + tensor input_271_cast_fp16 = reshape(shape = var_4153, x = attn_79_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor var_4158 = const()[name = tensor("op_4158"), val = tensor([1, 1])]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162_pad_type_0 = const()[name = tensor("op_4162_pad_type_0"), val = tensor("custom")]; + tensor var_4162_pad_0 = const()[name = tensor("op_4162_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(501611200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502840064))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502840256)))]; + tensor var_4162_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_4160, groups = var_1186, pad = var_4162_pad_0, pad_type = var_4162_pad_type_0, strides = var_4158, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("op_4162_cast_fp16")]; + tensor inputs_119_cast_fp16 = add(x = var_4162_cast_fp16, y = inputs_117_cast_fp16)[name = tensor("inputs_119_cast_fp16")]; + tensor input_273_axes_0 = const()[name = tensor("input_273_axes_0"), val = tensor([1])]; + tensor input_273_gamma_0_to_fp16 = const()[name = tensor("input_273_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502842880)))]; + tensor input_273_beta_0_to_fp16 = const()[name = tensor("input_273_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502845504)))]; + tensor var_4172_to_fp16 = const()[name = tensor("op_4172_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_273_cast_fp16 = layer_norm(axes = input_273_axes_0, beta = input_273_beta_0_to_fp16, epsilon = var_4172_to_fp16, gamma = input_273_gamma_0_to_fp16, x = inputs_119_cast_fp16)[name = tensor("input_273_cast_fp16")]; + tensor var_4188 = const()[name = tensor("op_4188"), val = tensor([1, 1])]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192_pad_type_0 = const()[name = tensor("op_4192_pad_type_0"), val = tensor("custom")]; + tensor var_4192_pad_0 = const()[name = tensor("op_4192_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(502848128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512678592))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512678784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512686528))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4192_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4190, groups = var_1186, pad = var_4192_pad_0, pad_type = var_4192_pad_type_0, strides = var_4188, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_273_cast_fp16)[name = tensor("op_4192_cast_fp16")]; + tensor var_4193_split_sizes_0 = const()[name = tensor("op_4193_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4193_axis_0 = const()[name = tensor("op_4193_axis_0"), val = tensor(1)]; + tensor var_4193_cast_fp16_0, tensor var_4193_cast_fp16_1 = split(axis = var_4193_axis_0, split_sizes = var_4193_split_sizes_0, x = var_4192_cast_fp16)[name = tensor("op_4193_cast_fp16")]; + tensor var_4195_mode_0 = const()[name = tensor("op_4195_mode_0"), val = tensor("EXACT")]; + tensor var_4195_cast_fp16 = gelu(mode = var_4195_mode_0, x = var_4193_cast_fp16_1)[name = tensor("op_4195_cast_fp16")]; + tensor input_275_cast_fp16 = mul(x = var_4193_cast_fp16_0, y = var_4195_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor var_4199 = const()[name = tensor("op_4199"), val = tensor([1, 1])]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203_pad_type_0 = const()[name = tensor("op_4203_pad_type_0"), val = tensor("custom")]; + tensor var_4203_pad_0 = const()[name = tensor("op_4203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512686720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517601984))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517602176)))]; + tensor var_4203_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_4201, groups = var_1186, pad = var_4203_pad_0, pad_type = var_4203_pad_type_0, strides = var_4199, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = tensor("op_4203_cast_fp16")]; + tensor inputs_121_cast_fp16 = add(x = var_4203_cast_fp16, y = inputs_119_cast_fp16)[name = tensor("inputs_121_cast_fp16")]; + tensor hidden_states_173_axes_0 = const()[name = tensor("hidden_states_173_axes_0"), val = tensor([1])]; + tensor hidden_states_173_gamma_0_to_fp16 = const()[name = tensor("hidden_states_173_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517604800)))]; + tensor hidden_states_173_beta_0_to_fp16 = const()[name = tensor("hidden_states_173_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517607424)))]; + tensor var_4219_to_fp16 = const()[name = tensor("op_4219_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_173_cast_fp16 = layer_norm(axes = hidden_states_173_axes_0, beta = hidden_states_173_beta_0_to_fp16, epsilon = var_4219_to_fp16, gamma = hidden_states_173_gamma_0_to_fp16, x = inputs_121_cast_fp16)[name = tensor("hidden_states_173_cast_fp16")]; + tensor var_4234 = const()[name = tensor("op_4234"), val = tensor([1, 1])]; + tensor var_4236 = const()[name = tensor("op_4236"), val = tensor([1, 1])]; + tensor q_81_pad_type_0 = const()[name = tensor("q_81_pad_type_0"), val = tensor("custom")]; + tensor q_81_pad_0 = const()[name = tensor("q_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(517610048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(518838912))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_81_cast_fp16 = conv(dilations = var_4236, groups = var_1186, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4234, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_173_cast_fp16)[name = tensor("q_81_cast_fp16")]; + tensor var_4240 = const()[name = tensor("op_4240"), val = tensor([1, 1])]; + tensor var_4242 = const()[name = tensor("op_4242"), val = tensor([1, 1])]; + tensor k_81_pad_type_0 = const()[name = tensor("k_81_pad_type_0"), val = tensor("custom")]; + tensor k_81_pad_0 = const()[name = tensor("k_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(518839104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520067968))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_81_cast_fp16 = conv(dilations = var_4242, groups = var_1186, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4240, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_173_cast_fp16)[name = tensor("k_81_cast_fp16")]; + tensor var_4246 = const()[name = tensor("op_4246"), val = tensor([1, 1])]; + tensor var_4248 = const()[name = tensor("op_4248"), val = tensor([1, 1])]; + tensor v_81_pad_type_0 = const()[name = tensor("v_81_pad_type_0"), val = tensor("custom")]; + tensor v_81_pad_0 = const()[name = tensor("v_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520068160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521297024))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_81_cast_fp16 = conv(dilations = var_4248, groups = var_1186, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4246, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_173_cast_fp16)[name = tensor("v_81_cast_fp16")]; + tensor var_4252 = const()[name = tensor("op_4252"), val = tensor([1, 20, 64, -1])]; + tensor var_4253_cast_fp16 = reshape(shape = var_4252, x = q_81_cast_fp16)[name = tensor("op_4253_cast_fp16")]; + tensor var_4254 = const()[name = tensor("op_4254"), val = tensor([1, 20, 64, -1])]; + tensor var_4255_cast_fp16 = reshape(shape = var_4254, x = k_81_cast_fp16)[name = tensor("op_4255_cast_fp16")]; + tensor var_4256 = const()[name = tensor("op_4256"), val = tensor([1, 20, 64, -1])]; + tensor var_4257_cast_fp16 = reshape(shape = var_4256, x = v_81_cast_fp16)[name = tensor("op_4257_cast_fp16")]; + tensor attn_weights_161_transpose_x_0 = const()[name = tensor("attn_weights_161_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_161_transpose_y_0 = const()[name = tensor("attn_weights_161_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4253_cast_fp16, y = var_4255_cast_fp16)[name = tensor("attn_weights_161_cast_fp16")]; + tensor attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_163_cast_fp16")]; + tensor var_4261_cast_fp16 = softmax(axis = var_1170, x = attn_weights_163_cast_fp16)[name = tensor("op_4261_cast_fp16")]; + tensor attn_81_transpose_x_0 = const()[name = tensor("attn_81_transpose_x_0"), val = tensor(false)]; + tensor attn_81_transpose_y_0 = const()[name = tensor("attn_81_transpose_y_0"), val = tensor(true)]; + tensor attn_81_cast_fp16 = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4257_cast_fp16, y = var_4261_cast_fp16)[name = tensor("attn_81_cast_fp16")]; + tensor var_4265 = const()[name = tensor("op_4265"), val = tensor([1, 1280, 1, -1])]; + tensor input_277_cast_fp16 = reshape(shape = var_4265, x = attn_81_cast_fp16)[name = tensor("input_277_cast_fp16")]; + tensor var_4270 = const()[name = tensor("op_4270"), val = tensor([1, 1])]; + tensor var_4272 = const()[name = tensor("op_4272"), val = tensor([1, 1])]; + tensor var_4274_pad_type_0 = const()[name = tensor("op_4274_pad_type_0"), val = tensor("custom")]; + tensor var_4274_pad_0 = const()[name = tensor("op_4274_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521297216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522526080))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522526272)))]; + tensor var_4274_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_4272, groups = var_1186, pad = var_4274_pad_0, pad_type = var_4274_pad_type_0, strides = var_4270, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = tensor("op_4274_cast_fp16")]; + tensor inputs_123_cast_fp16 = add(x = var_4274_cast_fp16, y = inputs_121_cast_fp16)[name = tensor("inputs_123_cast_fp16")]; + tensor hidden_states_175_axes_0 = const()[name = tensor("hidden_states_175_axes_0"), val = tensor([1])]; + tensor hidden_states_175_gamma_0_to_fp16 = const()[name = tensor("hidden_states_175_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522528896)))]; + tensor hidden_states_175_beta_0_to_fp16 = const()[name = tensor("hidden_states_175_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522531520)))]; + tensor var_4284_to_fp16 = const()[name = tensor("op_4284_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_175_cast_fp16 = layer_norm(axes = hidden_states_175_axes_0, beta = hidden_states_175_beta_0_to_fp16, epsilon = var_4284_to_fp16, gamma = hidden_states_175_gamma_0_to_fp16, x = inputs_123_cast_fp16)[name = tensor("hidden_states_175_cast_fp16")]; + tensor var_4299 = const()[name = tensor("op_4299"), val = tensor([1, 1])]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1, 1])]; + tensor q_83_pad_type_0 = const()[name = tensor("q_83_pad_type_0"), val = tensor("custom")]; + tensor q_83_pad_0 = const()[name = tensor("q_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(522534144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523763008))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_83_cast_fp16 = conv(dilations = var_4301, groups = var_1186, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4299, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_175_cast_fp16)[name = tensor("q_83_cast_fp16")]; + tensor var_4305 = const()[name = tensor("op_4305"), val = tensor([1, 1])]; + tensor var_4307 = const()[name = tensor("op_4307"), val = tensor([1, 1])]; + tensor k_83_pad_type_0 = const()[name = tensor("k_83_pad_type_0"), val = tensor("custom")]; + tensor k_83_pad_0 = const()[name = tensor("k_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523763200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(525729344))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_83_cast_fp16 = conv(dilations = var_4307, groups = var_1186, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4305, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_83_cast_fp16")]; + tensor var_4311 = const()[name = tensor("op_4311"), val = tensor([1, 1])]; + tensor var_4313 = const()[name = tensor("op_4313"), val = tensor([1, 1])]; + tensor v_83_pad_type_0 = const()[name = tensor("v_83_pad_type_0"), val = tensor("custom")]; + tensor v_83_pad_0 = const()[name = tensor("v_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(525729536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527695680))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_83_cast_fp16 = conv(dilations = var_4313, groups = var_1186, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4311, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_83_cast_fp16")]; + tensor var_4317 = const()[name = tensor("op_4317"), val = tensor([1, 20, 64, -1])]; + tensor var_4318_cast_fp16 = reshape(shape = var_4317, x = q_83_cast_fp16)[name = tensor("op_4318_cast_fp16")]; + tensor var_4319 = const()[name = tensor("op_4319"), val = tensor([1, 20, 64, -1])]; + tensor var_4320_cast_fp16 = reshape(shape = var_4319, x = k_83_cast_fp16)[name = tensor("op_4320_cast_fp16")]; + tensor var_4321 = const()[name = tensor("op_4321"), val = tensor([1, 20, 64, -1])]; + tensor var_4322_cast_fp16 = reshape(shape = var_4321, x = v_83_cast_fp16)[name = tensor("op_4322_cast_fp16")]; + tensor attn_weights_165_transpose_x_0 = const()[name = tensor("attn_weights_165_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_165_transpose_y_0 = const()[name = tensor("attn_weights_165_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_165_cast_fp16 = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4318_cast_fp16, y = var_4320_cast_fp16)[name = tensor("attn_weights_165_cast_fp16")]; + tensor attn_weights_167_cast_fp16 = mul(x = attn_weights_165_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_167_cast_fp16")]; + tensor var_4326_cast_fp16 = softmax(axis = var_1170, x = attn_weights_167_cast_fp16)[name = tensor("op_4326_cast_fp16")]; + tensor attn_83_transpose_x_0 = const()[name = tensor("attn_83_transpose_x_0"), val = tensor(false)]; + tensor attn_83_transpose_y_0 = const()[name = tensor("attn_83_transpose_y_0"), val = tensor(true)]; + tensor attn_83_cast_fp16 = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4322_cast_fp16, y = var_4326_cast_fp16)[name = tensor("attn_83_cast_fp16")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1280, 1, -1])]; + tensor input_279_cast_fp16 = reshape(shape = var_4330, x = attn_83_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor var_4335 = const()[name = tensor("op_4335"), val = tensor([1, 1])]; + tensor var_4337 = const()[name = tensor("op_4337"), val = tensor([1, 1])]; + tensor var_4339_pad_type_0 = const()[name = tensor("op_4339_pad_type_0"), val = tensor("custom")]; + tensor var_4339_pad_0 = const()[name = tensor("op_4339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527695872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528924736))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528924928)))]; + tensor var_4339_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_4337, groups = var_1186, pad = var_4339_pad_0, pad_type = var_4339_pad_type_0, strides = var_4335, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = tensor("op_4339_cast_fp16")]; + tensor inputs_125_cast_fp16 = add(x = var_4339_cast_fp16, y = inputs_123_cast_fp16)[name = tensor("inputs_125_cast_fp16")]; + tensor input_281_axes_0 = const()[name = tensor("input_281_axes_0"), val = tensor([1])]; + tensor input_281_gamma_0_to_fp16 = const()[name = tensor("input_281_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528927552)))]; + tensor input_281_beta_0_to_fp16 = const()[name = tensor("input_281_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528930176)))]; + tensor var_4349_to_fp16 = const()[name = tensor("op_4349_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = input_281_beta_0_to_fp16, epsilon = var_4349_to_fp16, gamma = input_281_gamma_0_to_fp16, x = inputs_125_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor var_4365 = const()[name = tensor("op_4365"), val = tensor([1, 1])]; + tensor var_4367 = const()[name = tensor("op_4367"), val = tensor([1, 1])]; + tensor var_4369_pad_type_0 = const()[name = tensor("op_4369_pad_type_0"), val = tensor("custom")]; + tensor var_4369_pad_0 = const()[name = tensor("op_4369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528932800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538763264))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538763456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538771200))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4369_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4367, groups = var_1186, pad = var_4369_pad_0, pad_type = var_4369_pad_type_0, strides = var_4365, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_281_cast_fp16)[name = tensor("op_4369_cast_fp16")]; + tensor var_4370_split_sizes_0 = const()[name = tensor("op_4370_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4370_axis_0 = const()[name = tensor("op_4370_axis_0"), val = tensor(1)]; + tensor var_4370_cast_fp16_0, tensor var_4370_cast_fp16_1 = split(axis = var_4370_axis_0, split_sizes = var_4370_split_sizes_0, x = var_4369_cast_fp16)[name = tensor("op_4370_cast_fp16")]; + tensor var_4372_mode_0 = const()[name = tensor("op_4372_mode_0"), val = tensor("EXACT")]; + tensor var_4372_cast_fp16 = gelu(mode = var_4372_mode_0, x = var_4370_cast_fp16_1)[name = tensor("op_4372_cast_fp16")]; + tensor input_283_cast_fp16 = mul(x = var_4370_cast_fp16_0, y = var_4372_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor var_4376 = const()[name = tensor("op_4376"), val = tensor([1, 1])]; + tensor var_4378 = const()[name = tensor("op_4378"), val = tensor([1, 1])]; + tensor var_4380_pad_type_0 = const()[name = tensor("op_4380_pad_type_0"), val = tensor("custom")]; + tensor var_4380_pad_0 = const()[name = tensor("op_4380_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538771392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543686656))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543686848)))]; + tensor var_4380_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_4378, groups = var_1186, pad = var_4380_pad_0, pad_type = var_4380_pad_type_0, strides = var_4376, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = tensor("op_4380_cast_fp16")]; + tensor inputs_127_cast_fp16 = add(x = var_4380_cast_fp16, y = inputs_125_cast_fp16)[name = tensor("inputs_127_cast_fp16")]; + tensor hidden_states_179_axes_0 = const()[name = tensor("hidden_states_179_axes_0"), val = tensor([1])]; + tensor hidden_states_179_gamma_0_to_fp16 = const()[name = tensor("hidden_states_179_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543689472)))]; + tensor hidden_states_179_beta_0_to_fp16 = const()[name = tensor("hidden_states_179_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543692096)))]; + tensor var_4396_to_fp16 = const()[name = tensor("op_4396_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_179_cast_fp16 = layer_norm(axes = hidden_states_179_axes_0, beta = hidden_states_179_beta_0_to_fp16, epsilon = var_4396_to_fp16, gamma = hidden_states_179_gamma_0_to_fp16, x = inputs_127_cast_fp16)[name = tensor("hidden_states_179_cast_fp16")]; + tensor var_4411 = const()[name = tensor("op_4411"), val = tensor([1, 1])]; + tensor var_4413 = const()[name = tensor("op_4413"), val = tensor([1, 1])]; + tensor q_85_pad_type_0 = const()[name = tensor("q_85_pad_type_0"), val = tensor("custom")]; + tensor q_85_pad_0 = const()[name = tensor("q_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543694720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544923584))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_85_cast_fp16 = conv(dilations = var_4413, groups = var_1186, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4411, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_179_cast_fp16)[name = tensor("q_85_cast_fp16")]; + tensor var_4417 = const()[name = tensor("op_4417"), val = tensor([1, 1])]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor k_85_pad_type_0 = const()[name = tensor("k_85_pad_type_0"), val = tensor("custom")]; + tensor k_85_pad_0 = const()[name = tensor("k_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544923776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546152640))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_85_cast_fp16 = conv(dilations = var_4419, groups = var_1186, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4417, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_179_cast_fp16)[name = tensor("k_85_cast_fp16")]; + tensor var_4423 = const()[name = tensor("op_4423"), val = tensor([1, 1])]; + tensor var_4425 = const()[name = tensor("op_4425"), val = tensor([1, 1])]; + tensor v_85_pad_type_0 = const()[name = tensor("v_85_pad_type_0"), val = tensor("custom")]; + tensor v_85_pad_0 = const()[name = tensor("v_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546152832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547381696))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_85_cast_fp16 = conv(dilations = var_4425, groups = var_1186, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4423, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_179_cast_fp16)[name = tensor("v_85_cast_fp16")]; + tensor var_4429 = const()[name = tensor("op_4429"), val = tensor([1, 20, 64, -1])]; + tensor var_4430_cast_fp16 = reshape(shape = var_4429, x = q_85_cast_fp16)[name = tensor("op_4430_cast_fp16")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([1, 20, 64, -1])]; + tensor var_4432_cast_fp16 = reshape(shape = var_4431, x = k_85_cast_fp16)[name = tensor("op_4432_cast_fp16")]; + tensor var_4433 = const()[name = tensor("op_4433"), val = tensor([1, 20, 64, -1])]; + tensor var_4434_cast_fp16 = reshape(shape = var_4433, x = v_85_cast_fp16)[name = tensor("op_4434_cast_fp16")]; + tensor attn_weights_169_transpose_x_0 = const()[name = tensor("attn_weights_169_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_169_transpose_y_0 = const()[name = tensor("attn_weights_169_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4430_cast_fp16, y = var_4432_cast_fp16)[name = tensor("attn_weights_169_cast_fp16")]; + tensor attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_171_cast_fp16")]; + tensor var_4438_cast_fp16 = softmax(axis = var_1170, x = attn_weights_171_cast_fp16)[name = tensor("op_4438_cast_fp16")]; + tensor attn_85_transpose_x_0 = const()[name = tensor("attn_85_transpose_x_0"), val = tensor(false)]; + tensor attn_85_transpose_y_0 = const()[name = tensor("attn_85_transpose_y_0"), val = tensor(true)]; + tensor attn_85_cast_fp16 = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4434_cast_fp16, y = var_4438_cast_fp16)[name = tensor("attn_85_cast_fp16")]; + tensor var_4442 = const()[name = tensor("op_4442"), val = tensor([1, 1280, 1, -1])]; + tensor input_285_cast_fp16 = reshape(shape = var_4442, x = attn_85_cast_fp16)[name = tensor("input_285_cast_fp16")]; + tensor var_4447 = const()[name = tensor("op_4447"), val = tensor([1, 1])]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451_pad_type_0 = const()[name = tensor("op_4451_pad_type_0"), val = tensor("custom")]; + tensor var_4451_pad_0 = const()[name = tensor("op_4451_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547381888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548610752))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548610944)))]; + tensor var_4451_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_4449, groups = var_1186, pad = var_4451_pad_0, pad_type = var_4451_pad_type_0, strides = var_4447, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_285_cast_fp16)[name = tensor("op_4451_cast_fp16")]; + tensor inputs_129_cast_fp16 = add(x = var_4451_cast_fp16, y = inputs_127_cast_fp16)[name = tensor("inputs_129_cast_fp16")]; + tensor hidden_states_181_axes_0 = const()[name = tensor("hidden_states_181_axes_0"), val = tensor([1])]; + tensor hidden_states_181_gamma_0_to_fp16 = const()[name = tensor("hidden_states_181_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548613568)))]; + tensor hidden_states_181_beta_0_to_fp16 = const()[name = tensor("hidden_states_181_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548616192)))]; + tensor var_4461_to_fp16 = const()[name = tensor("op_4461_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_181_cast_fp16 = layer_norm(axes = hidden_states_181_axes_0, beta = hidden_states_181_beta_0_to_fp16, epsilon = var_4461_to_fp16, gamma = hidden_states_181_gamma_0_to_fp16, x = inputs_129_cast_fp16)[name = tensor("hidden_states_181_cast_fp16")]; + tensor var_4476 = const()[name = tensor("op_4476"), val = tensor([1, 1])]; + tensor var_4478 = const()[name = tensor("op_4478"), val = tensor([1, 1])]; + tensor q_87_pad_type_0 = const()[name = tensor("q_87_pad_type_0"), val = tensor("custom")]; + tensor q_87_pad_0 = const()[name = tensor("q_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548618816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549847680))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_87_cast_fp16 = conv(dilations = var_4478, groups = var_1186, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4476, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_181_cast_fp16)[name = tensor("q_87_cast_fp16")]; + tensor var_4482 = const()[name = tensor("op_4482"), val = tensor([1, 1])]; + tensor var_4484 = const()[name = tensor("op_4484"), val = tensor([1, 1])]; + tensor k_87_pad_type_0 = const()[name = tensor("k_87_pad_type_0"), val = tensor("custom")]; + tensor k_87_pad_0 = const()[name = tensor("k_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549847872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(551814016))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_87_cast_fp16 = conv(dilations = var_4484, groups = var_1186, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4482, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_87_cast_fp16")]; + tensor var_4488 = const()[name = tensor("op_4488"), val = tensor([1, 1])]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor v_87_pad_type_0 = const()[name = tensor("v_87_pad_type_0"), val = tensor("custom")]; + tensor v_87_pad_0 = const()[name = tensor("v_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(551814208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553780352))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_87_cast_fp16 = conv(dilations = var_4490, groups = var_1186, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_4488, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_87_cast_fp16")]; + tensor var_4494 = const()[name = tensor("op_4494"), val = tensor([1, 20, 64, -1])]; + tensor var_4495_cast_fp16 = reshape(shape = var_4494, x = q_87_cast_fp16)[name = tensor("op_4495_cast_fp16")]; + tensor var_4496 = const()[name = tensor("op_4496"), val = tensor([1, 20, 64, -1])]; + tensor var_4497_cast_fp16 = reshape(shape = var_4496, x = k_87_cast_fp16)[name = tensor("op_4497_cast_fp16")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 20, 64, -1])]; + tensor var_4499_cast_fp16 = reshape(shape = var_4498, x = v_87_cast_fp16)[name = tensor("op_4499_cast_fp16")]; + tensor attn_weights_173_transpose_x_0 = const()[name = tensor("attn_weights_173_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_173_transpose_y_0 = const()[name = tensor("attn_weights_173_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_173_cast_fp16 = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_4495_cast_fp16, y = var_4497_cast_fp16)[name = tensor("attn_weights_173_cast_fp16")]; + tensor attn_weights_175_cast_fp16 = mul(x = attn_weights_173_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_175_cast_fp16")]; + tensor var_4503_cast_fp16 = softmax(axis = var_1170, x = attn_weights_175_cast_fp16)[name = tensor("op_4503_cast_fp16")]; + tensor attn_87_transpose_x_0 = const()[name = tensor("attn_87_transpose_x_0"), val = tensor(false)]; + tensor attn_87_transpose_y_0 = const()[name = tensor("attn_87_transpose_y_0"), val = tensor(true)]; + tensor attn_87_cast_fp16 = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4499_cast_fp16, y = var_4503_cast_fp16)[name = tensor("attn_87_cast_fp16")]; + tensor var_4507 = const()[name = tensor("op_4507"), val = tensor([1, 1280, 1, -1])]; + tensor input_287_cast_fp16 = reshape(shape = var_4507, x = attn_87_cast_fp16)[name = tensor("input_287_cast_fp16")]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor var_4514 = const()[name = tensor("op_4514"), val = tensor([1, 1])]; + tensor var_4516_pad_type_0 = const()[name = tensor("op_4516_pad_type_0"), val = tensor("custom")]; + tensor var_4516_pad_0 = const()[name = tensor("op_4516_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(553780544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555009408))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555009600)))]; + tensor var_4516_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_4514, groups = var_1186, pad = var_4516_pad_0, pad_type = var_4516_pad_type_0, strides = var_4512, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = tensor("op_4516_cast_fp16")]; + tensor inputs_131_cast_fp16 = add(x = var_4516_cast_fp16, y = inputs_129_cast_fp16)[name = tensor("inputs_131_cast_fp16")]; + tensor input_289_axes_0 = const()[name = tensor("input_289_axes_0"), val = tensor([1])]; + tensor input_289_gamma_0_to_fp16 = const()[name = tensor("input_289_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555012224)))]; + tensor input_289_beta_0_to_fp16 = const()[name = tensor("input_289_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555014848)))]; + tensor var_4526_to_fp16 = const()[name = tensor("op_4526_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = input_289_beta_0_to_fp16, epsilon = var_4526_to_fp16, gamma = input_289_gamma_0_to_fp16, x = inputs_131_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor var_4542 = const()[name = tensor("op_4542"), val = tensor([1, 1])]; + tensor var_4544 = const()[name = tensor("op_4544"), val = tensor([1, 1])]; + tensor var_4546_pad_type_0 = const()[name = tensor("op_4546_pad_type_0"), val = tensor("custom")]; + tensor var_4546_pad_0 = const()[name = tensor("op_4546_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555017472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564847936))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564848128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564855872))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4546_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4544, groups = var_1186, pad = var_4546_pad_0, pad_type = var_4546_pad_type_0, strides = var_4542, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_289_cast_fp16)[name = tensor("op_4546_cast_fp16")]; + tensor var_4547_split_sizes_0 = const()[name = tensor("op_4547_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4547_axis_0 = const()[name = tensor("op_4547_axis_0"), val = tensor(1)]; + tensor var_4547_cast_fp16_0, tensor var_4547_cast_fp16_1 = split(axis = var_4547_axis_0, split_sizes = var_4547_split_sizes_0, x = var_4546_cast_fp16)[name = tensor("op_4547_cast_fp16")]; + tensor var_4549_mode_0 = const()[name = tensor("op_4549_mode_0"), val = tensor("EXACT")]; + tensor var_4549_cast_fp16 = gelu(mode = var_4549_mode_0, x = var_4547_cast_fp16_1)[name = tensor("op_4549_cast_fp16")]; + tensor input_291_cast_fp16 = mul(x = var_4547_cast_fp16_0, y = var_4549_cast_fp16)[name = tensor("input_291_cast_fp16")]; + tensor var_4553 = const()[name = tensor("op_4553"), val = tensor([1, 1])]; + tensor var_4555 = const()[name = tensor("op_4555"), val = tensor([1, 1])]; + tensor var_4557_pad_type_0 = const()[name = tensor("op_4557_pad_type_0"), val = tensor("custom")]; + tensor var_4557_pad_0 = const()[name = tensor("op_4557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564856064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569771328))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569771520)))]; + tensor var_4557_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_4555, groups = var_1186, pad = var_4557_pad_0, pad_type = var_4557_pad_type_0, strides = var_4553, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = tensor("op_4557_cast_fp16")]; + tensor inputs_133_cast_fp16 = add(x = var_4557_cast_fp16, y = inputs_131_cast_fp16)[name = tensor("inputs_133_cast_fp16")]; + tensor hidden_states_185_axes_0 = const()[name = tensor("hidden_states_185_axes_0"), val = tensor([1])]; + tensor hidden_states_185_gamma_0_to_fp16 = const()[name = tensor("hidden_states_185_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569774144)))]; + tensor hidden_states_185_beta_0_to_fp16 = const()[name = tensor("hidden_states_185_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569776768)))]; + tensor var_4573_to_fp16 = const()[name = tensor("op_4573_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_185_cast_fp16 = layer_norm(axes = hidden_states_185_axes_0, beta = hidden_states_185_beta_0_to_fp16, epsilon = var_4573_to_fp16, gamma = hidden_states_185_gamma_0_to_fp16, x = inputs_133_cast_fp16)[name = tensor("hidden_states_185_cast_fp16")]; + tensor var_4588 = const()[name = tensor("op_4588"), val = tensor([1, 1])]; + tensor var_4590 = const()[name = tensor("op_4590"), val = tensor([1, 1])]; + tensor q_89_pad_type_0 = const()[name = tensor("q_89_pad_type_0"), val = tensor("custom")]; + tensor q_89_pad_0 = const()[name = tensor("q_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569779392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571008256))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_89_cast_fp16 = conv(dilations = var_4590, groups = var_1186, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_4588, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_185_cast_fp16)[name = tensor("q_89_cast_fp16")]; + tensor var_4594 = const()[name = tensor("op_4594"), val = tensor([1, 1])]; + tensor var_4596 = const()[name = tensor("op_4596"), val = tensor([1, 1])]; + tensor k_89_pad_type_0 = const()[name = tensor("k_89_pad_type_0"), val = tensor("custom")]; + tensor k_89_pad_0 = const()[name = tensor("k_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571008448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572237312))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_89_cast_fp16 = conv(dilations = var_4596, groups = var_1186, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_4594, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_185_cast_fp16)[name = tensor("k_89_cast_fp16")]; + tensor var_4600 = const()[name = tensor("op_4600"), val = tensor([1, 1])]; + tensor var_4602 = const()[name = tensor("op_4602"), val = tensor([1, 1])]; + tensor v_89_pad_type_0 = const()[name = tensor("v_89_pad_type_0"), val = tensor("custom")]; + tensor v_89_pad_0 = const()[name = tensor("v_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572237504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573466368))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_89_cast_fp16 = conv(dilations = var_4602, groups = var_1186, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_4600, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_185_cast_fp16)[name = tensor("v_89_cast_fp16")]; + tensor var_4606 = const()[name = tensor("op_4606"), val = tensor([1, 20, 64, -1])]; + tensor var_4607_cast_fp16 = reshape(shape = var_4606, x = q_89_cast_fp16)[name = tensor("op_4607_cast_fp16")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([1, 20, 64, -1])]; + tensor var_4609_cast_fp16 = reshape(shape = var_4608, x = k_89_cast_fp16)[name = tensor("op_4609_cast_fp16")]; + tensor var_4610 = const()[name = tensor("op_4610"), val = tensor([1, 20, 64, -1])]; + tensor var_4611_cast_fp16 = reshape(shape = var_4610, x = v_89_cast_fp16)[name = tensor("op_4611_cast_fp16")]; + tensor attn_weights_177_transpose_x_0 = const()[name = tensor("attn_weights_177_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_177_transpose_y_0 = const()[name = tensor("attn_weights_177_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4607_cast_fp16, y = var_4609_cast_fp16)[name = tensor("attn_weights_177_cast_fp16")]; + tensor attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_179_cast_fp16")]; + tensor var_4615_cast_fp16 = softmax(axis = var_1170, x = attn_weights_179_cast_fp16)[name = tensor("op_4615_cast_fp16")]; + tensor attn_89_transpose_x_0 = const()[name = tensor("attn_89_transpose_x_0"), val = tensor(false)]; + tensor attn_89_transpose_y_0 = const()[name = tensor("attn_89_transpose_y_0"), val = tensor(true)]; + tensor attn_89_cast_fp16 = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_4611_cast_fp16, y = var_4615_cast_fp16)[name = tensor("attn_89_cast_fp16")]; + tensor var_4619 = const()[name = tensor("op_4619"), val = tensor([1, 1280, 1, -1])]; + tensor input_293_cast_fp16 = reshape(shape = var_4619, x = attn_89_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor var_4624 = const()[name = tensor("op_4624"), val = tensor([1, 1])]; + tensor var_4626 = const()[name = tensor("op_4626"), val = tensor([1, 1])]; + tensor var_4628_pad_type_0 = const()[name = tensor("op_4628_pad_type_0"), val = tensor("custom")]; + tensor var_4628_pad_0 = const()[name = tensor("op_4628_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573466560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574695424))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574695616)))]; + tensor var_4628_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_4626, groups = var_1186, pad = var_4628_pad_0, pad_type = var_4628_pad_type_0, strides = var_4624, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_293_cast_fp16)[name = tensor("op_4628_cast_fp16")]; + tensor inputs_135_cast_fp16 = add(x = var_4628_cast_fp16, y = inputs_133_cast_fp16)[name = tensor("inputs_135_cast_fp16")]; + tensor hidden_states_187_axes_0 = const()[name = tensor("hidden_states_187_axes_0"), val = tensor([1])]; + tensor hidden_states_187_gamma_0_to_fp16 = const()[name = tensor("hidden_states_187_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574698240)))]; + tensor hidden_states_187_beta_0_to_fp16 = const()[name = tensor("hidden_states_187_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574700864)))]; + tensor var_4638_to_fp16 = const()[name = tensor("op_4638_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_187_cast_fp16 = layer_norm(axes = hidden_states_187_axes_0, beta = hidden_states_187_beta_0_to_fp16, epsilon = var_4638_to_fp16, gamma = hidden_states_187_gamma_0_to_fp16, x = inputs_135_cast_fp16)[name = tensor("hidden_states_187_cast_fp16")]; + tensor var_4653 = const()[name = tensor("op_4653"), val = tensor([1, 1])]; + tensor var_4655 = const()[name = tensor("op_4655"), val = tensor([1, 1])]; + tensor q_91_pad_type_0 = const()[name = tensor("q_91_pad_type_0"), val = tensor("custom")]; + tensor q_91_pad_0 = const()[name = tensor("q_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574703488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575932352))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_91_cast_fp16 = conv(dilations = var_4655, groups = var_1186, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_4653, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast_fp16)[name = tensor("q_91_cast_fp16")]; + tensor var_4659 = const()[name = tensor("op_4659"), val = tensor([1, 1])]; + tensor var_4661 = const()[name = tensor("op_4661"), val = tensor([1, 1])]; + tensor k_91_pad_type_0 = const()[name = tensor("k_91_pad_type_0"), val = tensor("custom")]; + tensor k_91_pad_0 = const()[name = tensor("k_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575932544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577898688))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_91_cast_fp16 = conv(dilations = var_4661, groups = var_1186, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_4659, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_91_cast_fp16")]; + tensor var_4665 = const()[name = tensor("op_4665"), val = tensor([1, 1])]; + tensor var_4667 = const()[name = tensor("op_4667"), val = tensor([1, 1])]; + tensor v_91_pad_type_0 = const()[name = tensor("v_91_pad_type_0"), val = tensor("custom")]; + tensor v_91_pad_0 = const()[name = tensor("v_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577898880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579865024))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_91_cast_fp16 = conv(dilations = var_4667, groups = var_1186, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_4665, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_91_cast_fp16")]; + tensor var_4671 = const()[name = tensor("op_4671"), val = tensor([1, 20, 64, -1])]; + tensor var_4672_cast_fp16 = reshape(shape = var_4671, x = q_91_cast_fp16)[name = tensor("op_4672_cast_fp16")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([1, 20, 64, -1])]; + tensor var_4674_cast_fp16 = reshape(shape = var_4673, x = k_91_cast_fp16)[name = tensor("op_4674_cast_fp16")]; + tensor var_4675 = const()[name = tensor("op_4675"), val = tensor([1, 20, 64, -1])]; + tensor var_4676_cast_fp16 = reshape(shape = var_4675, x = v_91_cast_fp16)[name = tensor("op_4676_cast_fp16")]; + tensor attn_weights_181_transpose_x_0 = const()[name = tensor("attn_weights_181_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_181_transpose_y_0 = const()[name = tensor("attn_weights_181_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_181_cast_fp16 = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_4672_cast_fp16, y = var_4674_cast_fp16)[name = tensor("attn_weights_181_cast_fp16")]; + tensor attn_weights_183_cast_fp16 = mul(x = attn_weights_181_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_183_cast_fp16")]; + tensor var_4680_cast_fp16 = softmax(axis = var_1170, x = attn_weights_183_cast_fp16)[name = tensor("op_4680_cast_fp16")]; + tensor attn_91_transpose_x_0 = const()[name = tensor("attn_91_transpose_x_0"), val = tensor(false)]; + tensor attn_91_transpose_y_0 = const()[name = tensor("attn_91_transpose_y_0"), val = tensor(true)]; + tensor attn_91_cast_fp16 = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_4676_cast_fp16, y = var_4680_cast_fp16)[name = tensor("attn_91_cast_fp16")]; + tensor var_4684 = const()[name = tensor("op_4684"), val = tensor([1, 1280, 1, -1])]; + tensor input_295_cast_fp16 = reshape(shape = var_4684, x = attn_91_cast_fp16)[name = tensor("input_295_cast_fp16")]; + tensor var_4689 = const()[name = tensor("op_4689"), val = tensor([1, 1])]; + tensor var_4691 = const()[name = tensor("op_4691"), val = tensor([1, 1])]; + tensor var_4693_pad_type_0 = const()[name = tensor("op_4693_pad_type_0"), val = tensor("custom")]; + tensor var_4693_pad_0 = const()[name = tensor("op_4693_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579865216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581094080))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581094272)))]; + tensor var_4693_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_4691, groups = var_1186, pad = var_4693_pad_0, pad_type = var_4693_pad_type_0, strides = var_4689, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = tensor("op_4693_cast_fp16")]; + tensor inputs_137_cast_fp16 = add(x = var_4693_cast_fp16, y = inputs_135_cast_fp16)[name = tensor("inputs_137_cast_fp16")]; + tensor input_297_axes_0 = const()[name = tensor("input_297_axes_0"), val = tensor([1])]; + tensor input_297_gamma_0_to_fp16 = const()[name = tensor("input_297_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581096896)))]; + tensor input_297_beta_0_to_fp16 = const()[name = tensor("input_297_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581099520)))]; + tensor var_4703_to_fp16 = const()[name = tensor("op_4703_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_297_cast_fp16 = layer_norm(axes = input_297_axes_0, beta = input_297_beta_0_to_fp16, epsilon = var_4703_to_fp16, gamma = input_297_gamma_0_to_fp16, x = inputs_137_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(581102144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590932608))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590932800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590940544))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4723_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4721, groups = var_1186, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_297_cast_fp16)[name = tensor("op_4723_cast_fp16")]; + tensor var_4724_split_sizes_0 = const()[name = tensor("op_4724_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4724_axis_0 = const()[name = tensor("op_4724_axis_0"), val = tensor(1)]; + tensor var_4724_cast_fp16_0, tensor var_4724_cast_fp16_1 = split(axis = var_4724_axis_0, split_sizes = var_4724_split_sizes_0, x = var_4723_cast_fp16)[name = tensor("op_4724_cast_fp16")]; + tensor var_4726_mode_0 = const()[name = tensor("op_4726_mode_0"), val = tensor("EXACT")]; + tensor var_4726_cast_fp16 = gelu(mode = var_4726_mode_0, x = var_4724_cast_fp16_1)[name = tensor("op_4726_cast_fp16")]; + tensor input_299_cast_fp16 = mul(x = var_4724_cast_fp16_0, y = var_4726_cast_fp16)[name = tensor("input_299_cast_fp16")]; + tensor var_4730 = const()[name = tensor("op_4730"), val = tensor([1, 1])]; + tensor var_4732 = const()[name = tensor("op_4732"), val = tensor([1, 1])]; + tensor var_4734_pad_type_0 = const()[name = tensor("op_4734_pad_type_0"), val = tensor("custom")]; + tensor var_4734_pad_0 = const()[name = tensor("op_4734_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590940736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595856000))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595856192)))]; + tensor var_4734_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_4732, groups = var_1186, pad = var_4734_pad_0, pad_type = var_4734_pad_type_0, strides = var_4730, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = tensor("op_4734_cast_fp16")]; + tensor inputs_139_cast_fp16 = add(x = var_4734_cast_fp16, y = inputs_137_cast_fp16)[name = tensor("inputs_139_cast_fp16")]; + tensor hidden_states_191_axes_0 = const()[name = tensor("hidden_states_191_axes_0"), val = tensor([1])]; + tensor hidden_states_191_gamma_0_to_fp16 = const()[name = tensor("hidden_states_191_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595858816)))]; + tensor hidden_states_191_beta_0_to_fp16 = const()[name = tensor("hidden_states_191_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595861440)))]; + tensor var_4750_to_fp16 = const()[name = tensor("op_4750_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_191_cast_fp16 = layer_norm(axes = hidden_states_191_axes_0, beta = hidden_states_191_beta_0_to_fp16, epsilon = var_4750_to_fp16, gamma = hidden_states_191_gamma_0_to_fp16, x = inputs_139_cast_fp16)[name = tensor("hidden_states_191_cast_fp16")]; + tensor var_4765 = const()[name = tensor("op_4765"), val = tensor([1, 1])]; + tensor var_4767 = const()[name = tensor("op_4767"), val = tensor([1, 1])]; + tensor q_93_pad_type_0 = const()[name = tensor("q_93_pad_type_0"), val = tensor("custom")]; + tensor q_93_pad_0 = const()[name = tensor("q_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595864064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(597092928))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_93_cast_fp16 = conv(dilations = var_4767, groups = var_1186, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_4765, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_191_cast_fp16)[name = tensor("q_93_cast_fp16")]; + tensor var_4771 = const()[name = tensor("op_4771"), val = tensor([1, 1])]; + tensor var_4773 = const()[name = tensor("op_4773"), val = tensor([1, 1])]; + tensor k_93_pad_type_0 = const()[name = tensor("k_93_pad_type_0"), val = tensor("custom")]; + tensor k_93_pad_0 = const()[name = tensor("k_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(597093120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598321984))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_93_cast_fp16 = conv(dilations = var_4773, groups = var_1186, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_4771, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_191_cast_fp16)[name = tensor("k_93_cast_fp16")]; + tensor var_4777 = const()[name = tensor("op_4777"), val = tensor([1, 1])]; + tensor var_4779 = const()[name = tensor("op_4779"), val = tensor([1, 1])]; + tensor v_93_pad_type_0 = const()[name = tensor("v_93_pad_type_0"), val = tensor("custom")]; + tensor v_93_pad_0 = const()[name = tensor("v_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598322176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599551040))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_93_cast_fp16 = conv(dilations = var_4779, groups = var_1186, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_4777, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_191_cast_fp16)[name = tensor("v_93_cast_fp16")]; + tensor var_4783 = const()[name = tensor("op_4783"), val = tensor([1, 20, 64, -1])]; + tensor var_4784_cast_fp16 = reshape(shape = var_4783, x = q_93_cast_fp16)[name = tensor("op_4784_cast_fp16")]; + tensor var_4785 = const()[name = tensor("op_4785"), val = tensor([1, 20, 64, -1])]; + tensor var_4786_cast_fp16 = reshape(shape = var_4785, x = k_93_cast_fp16)[name = tensor("op_4786_cast_fp16")]; + tensor var_4787 = const()[name = tensor("op_4787"), val = tensor([1, 20, 64, -1])]; + tensor var_4788_cast_fp16 = reshape(shape = var_4787, x = v_93_cast_fp16)[name = tensor("op_4788_cast_fp16")]; + tensor attn_weights_185_transpose_x_0 = const()[name = tensor("attn_weights_185_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_185_transpose_y_0 = const()[name = tensor("attn_weights_185_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4784_cast_fp16, y = var_4786_cast_fp16)[name = tensor("attn_weights_185_cast_fp16")]; + tensor attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_187_cast_fp16")]; + tensor var_4792_cast_fp16 = softmax(axis = var_1170, x = attn_weights_187_cast_fp16)[name = tensor("op_4792_cast_fp16")]; + tensor attn_93_transpose_x_0 = const()[name = tensor("attn_93_transpose_x_0"), val = tensor(false)]; + tensor attn_93_transpose_y_0 = const()[name = tensor("attn_93_transpose_y_0"), val = tensor(true)]; + tensor attn_93_cast_fp16 = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_4788_cast_fp16, y = var_4792_cast_fp16)[name = tensor("attn_93_cast_fp16")]; + tensor var_4796 = const()[name = tensor("op_4796"), val = tensor([1, 1280, 1, -1])]; + tensor input_301_cast_fp16 = reshape(shape = var_4796, x = attn_93_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor var_4801 = const()[name = tensor("op_4801"), val = tensor([1, 1])]; + tensor var_4803 = const()[name = tensor("op_4803"), val = tensor([1, 1])]; + tensor var_4805_pad_type_0 = const()[name = tensor("op_4805_pad_type_0"), val = tensor("custom")]; + tensor var_4805_pad_0 = const()[name = tensor("op_4805_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599551232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600780096))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600780288)))]; + tensor var_4805_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_4803, groups = var_1186, pad = var_4805_pad_0, pad_type = var_4805_pad_type_0, strides = var_4801, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_301_cast_fp16)[name = tensor("op_4805_cast_fp16")]; + tensor inputs_141_cast_fp16 = add(x = var_4805_cast_fp16, y = inputs_139_cast_fp16)[name = tensor("inputs_141_cast_fp16")]; + tensor hidden_states_193_axes_0 = const()[name = tensor("hidden_states_193_axes_0"), val = tensor([1])]; + tensor hidden_states_193_gamma_0_to_fp16 = const()[name = tensor("hidden_states_193_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600782912)))]; + tensor hidden_states_193_beta_0_to_fp16 = const()[name = tensor("hidden_states_193_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600785536)))]; + tensor var_4815_to_fp16 = const()[name = tensor("op_4815_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_193_cast_fp16 = layer_norm(axes = hidden_states_193_axes_0, beta = hidden_states_193_beta_0_to_fp16, epsilon = var_4815_to_fp16, gamma = hidden_states_193_gamma_0_to_fp16, x = inputs_141_cast_fp16)[name = tensor("hidden_states_193_cast_fp16")]; + tensor var_4830 = const()[name = tensor("op_4830"), val = tensor([1, 1])]; + tensor var_4832 = const()[name = tensor("op_4832"), val = tensor([1, 1])]; + tensor q_95_pad_type_0 = const()[name = tensor("q_95_pad_type_0"), val = tensor("custom")]; + tensor q_95_pad_0 = const()[name = tensor("q_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600788160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602017024))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_95_cast_fp16 = conv(dilations = var_4832, groups = var_1186, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_4830, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_193_cast_fp16)[name = tensor("q_95_cast_fp16")]; + tensor var_4836 = const()[name = tensor("op_4836"), val = tensor([1, 1])]; + tensor var_4838 = const()[name = tensor("op_4838"), val = tensor([1, 1])]; + tensor k_95_pad_type_0 = const()[name = tensor("k_95_pad_type_0"), val = tensor("custom")]; + tensor k_95_pad_0 = const()[name = tensor("k_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602017216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603983360))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_95_cast_fp16 = conv(dilations = var_4838, groups = var_1186, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_4836, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_95_cast_fp16")]; + tensor var_4842 = const()[name = tensor("op_4842"), val = tensor([1, 1])]; + tensor var_4844 = const()[name = tensor("op_4844"), val = tensor([1, 1])]; + tensor v_95_pad_type_0 = const()[name = tensor("v_95_pad_type_0"), val = tensor("custom")]; + tensor v_95_pad_0 = const()[name = tensor("v_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603983552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605949696))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_95_cast_fp16 = conv(dilations = var_4844, groups = var_1186, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_4842, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_95_cast_fp16")]; + tensor var_4848 = const()[name = tensor("op_4848"), val = tensor([1, 20, 64, -1])]; + tensor var_4849_cast_fp16 = reshape(shape = var_4848, x = q_95_cast_fp16)[name = tensor("op_4849_cast_fp16")]; + tensor var_4850 = const()[name = tensor("op_4850"), val = tensor([1, 20, 64, -1])]; + tensor var_4851_cast_fp16 = reshape(shape = var_4850, x = k_95_cast_fp16)[name = tensor("op_4851_cast_fp16")]; + tensor var_4852 = const()[name = tensor("op_4852"), val = tensor([1, 20, 64, -1])]; + tensor var_4853_cast_fp16 = reshape(shape = var_4852, x = v_95_cast_fp16)[name = tensor("op_4853_cast_fp16")]; + tensor attn_weights_189_transpose_x_0 = const()[name = tensor("attn_weights_189_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_189_transpose_y_0 = const()[name = tensor("attn_weights_189_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_189_cast_fp16 = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_4849_cast_fp16, y = var_4851_cast_fp16)[name = tensor("attn_weights_189_cast_fp16")]; + tensor attn_weights_191_cast_fp16 = mul(x = attn_weights_189_cast_fp16, y = var_1177_to_fp16)[name = tensor("attn_weights_191_cast_fp16")]; + tensor var_4857_cast_fp16 = softmax(axis = var_1170, x = attn_weights_191_cast_fp16)[name = tensor("op_4857_cast_fp16")]; + tensor attn_95_transpose_x_0 = const()[name = tensor("attn_95_transpose_x_0"), val = tensor(false)]; + tensor attn_95_transpose_y_0 = const()[name = tensor("attn_95_transpose_y_0"), val = tensor(true)]; + tensor attn_95_cast_fp16 = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_4853_cast_fp16, y = var_4857_cast_fp16)[name = tensor("attn_95_cast_fp16")]; + tensor var_4861 = const()[name = tensor("op_4861"), val = tensor([1, 1280, 1, -1])]; + tensor input_303_cast_fp16 = reshape(shape = var_4861, x = attn_95_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor var_4866 = const()[name = tensor("op_4866"), val = tensor([1, 1])]; + tensor var_4868 = const()[name = tensor("op_4868"), val = tensor([1, 1])]; + tensor var_4870_pad_type_0 = const()[name = tensor("op_4870_pad_type_0"), val = tensor("custom")]; + tensor var_4870_pad_0 = const()[name = tensor("op_4870_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605949888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607178752))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607178944)))]; + tensor var_4870_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_4868, groups = var_1186, pad = var_4870_pad_0, pad_type = var_4870_pad_type_0, strides = var_4866, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = tensor("op_4870_cast_fp16")]; + tensor inputs_143_cast_fp16 = add(x = var_4870_cast_fp16, y = inputs_141_cast_fp16)[name = tensor("inputs_143_cast_fp16")]; + tensor input_305_axes_0 = const()[name = tensor("input_305_axes_0"), val = tensor([1])]; + tensor input_305_gamma_0_to_fp16 = const()[name = tensor("input_305_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607181568)))]; + tensor input_305_beta_0_to_fp16 = const()[name = tensor("input_305_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607184192)))]; + tensor var_4880_to_fp16 = const()[name = tensor("op_4880_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_305_cast_fp16 = layer_norm(axes = input_305_axes_0, beta = input_305_beta_0_to_fp16, epsilon = var_4880_to_fp16, gamma = input_305_gamma_0_to_fp16, x = inputs_143_cast_fp16)[name = tensor("input_305_cast_fp16")]; + tensor var_4896 = const()[name = tensor("op_4896"), val = tensor([1, 1])]; + tensor var_4898 = const()[name = tensor("op_4898"), val = tensor([1, 1])]; + tensor var_4900_pad_type_0 = const()[name = tensor("op_4900_pad_type_0"), val = tensor("custom")]; + tensor var_4900_pad_0 = const()[name = tensor("op_4900_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607186816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617017280))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617017472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617025216))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_4900_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4898, groups = var_1186, pad = var_4900_pad_0, pad_type = var_4900_pad_type_0, strides = var_4896, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_305_cast_fp16)[name = tensor("op_4900_cast_fp16")]; + tensor var_4901_split_sizes_0 = const()[name = tensor("op_4901_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4901_axis_0 = const()[name = tensor("op_4901_axis_0"), val = tensor(1)]; + tensor var_4901_cast_fp16_0, tensor var_4901_cast_fp16_1 = split(axis = var_4901_axis_0, split_sizes = var_4901_split_sizes_0, x = var_4900_cast_fp16)[name = tensor("op_4901_cast_fp16")]; + tensor var_4903_mode_0 = const()[name = tensor("op_4903_mode_0"), val = tensor("EXACT")]; + tensor var_4903_cast_fp16 = gelu(mode = var_4903_mode_0, x = var_4901_cast_fp16_1)[name = tensor("op_4903_cast_fp16")]; + tensor input_307_cast_fp16 = mul(x = var_4901_cast_fp16_0, y = var_4903_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor var_4907 = const()[name = tensor("op_4907"), val = tensor([1, 1])]; + tensor var_4909 = const()[name = tensor("op_4909"), val = tensor([1, 1])]; + tensor var_4911_pad_type_0 = const()[name = tensor("op_4911_pad_type_0"), val = tensor("custom")]; + tensor var_4911_pad_0 = const()[name = tensor("op_4911_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617025408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621940672))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621940864)))]; + tensor var_4911_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_4909, groups = var_1186, pad = var_4911_pad_0, pad_type = var_4911_pad_type_0, strides = var_4907, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("op_4911_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = var_4911_cast_fp16, y = inputs_143_cast_fp16)[name = tensor("hidden_states_197_cast_fp16")]; + tensor var_4913 = const()[name = tensor("op_4913"), val = tensor([1, 1280, 32, 32])]; + tensor input_309_cast_fp16 = reshape(shape = var_4913, x = hidden_states_197_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor var_4917 = const()[name = tensor("op_4917"), val = tensor([1, 1])]; + tensor var_4919 = const()[name = tensor("op_4919"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621943488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623172352))), name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623172544)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_4919, groups = var_1186, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4917, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_309_cast_fp16)[name = tensor("hidden_states_199_cast_fp16")]; + tensor input_311_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_133_cast_fp16)[name = tensor("input_311_cast_fp16")]; + tensor var_4927 = const()[name = tensor("op_4927"), val = tensor(3)]; + tensor var_4943 = const()[name = tensor("op_4943"), val = tensor(1)]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_311_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623175168)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623177792)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_315_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor var_4961 = const()[name = tensor("op_4961"), val = tensor([1, 1])]; + tensor var_4963 = const()[name = tensor("op_4963"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623180416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634239680))), name = tensor("mid_block_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634239872)))]; + tensor hidden_states_201_cast_fp16 = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_4963, groups = var_4943, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_4961, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_315_cast_fp16)[name = tensor("hidden_states_201_cast_fp16")]; + tensor var_4969 = const()[name = tensor("op_4969"), val = tensor([1, 1])]; + tensor var_4971 = const()[name = tensor("op_4971"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634242496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635471360))), name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635471552)))]; + tensor temb_13_cast_fp16 = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4971, groups = var_4943, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_4969, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_13_cast_fp16")]; + tensor input_319_cast_fp16 = add(x = hidden_states_201_cast_fp16, y = temb_13_cast_fp16)[name = tensor("input_319_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_319_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635474176)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635476800)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor var_4981 = const()[name = tensor("op_4981"), val = tensor([1, 1])]; + tensor var_4983 = const()[name = tensor("op_4983"), val = tensor([1, 1])]; + tensor hidden_states_203_pad_type_0 = const()[name = tensor("hidden_states_203_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_203_pad_0 = const()[name = tensor("hidden_states_203_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635479424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(646538688))), name = tensor("mid_block_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(646538880)))]; + tensor hidden_states_203_cast_fp16 = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_4983, groups = var_4943, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_4981, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = tensor("hidden_states_203_cast_fp16")]; + tensor hidden_states_205_cast_fp16 = add(x = input_311_cast_fp16, y = hidden_states_203_cast_fp16)[name = tensor("hidden_states_205_cast_fp16")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = hidden_states_205_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(646541504)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(646544128)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor var_5021 = const()[name = tensor("op_5021"), val = tensor([1, 1])]; + tensor var_5023 = const()[name = tensor("op_5023"), val = tensor([1, 1])]; + tensor hidden_states_207_pad_type_0 = const()[name = tensor("hidden_states_207_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_207_pad_0 = const()[name = tensor("hidden_states_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(646546752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647775616))), name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647775808)))]; + tensor hidden_states_207_cast_fp16 = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_5023, groups = var_4943, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = var_5021, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_37_cast_fp16)[name = tensor("hidden_states_207_cast_fp16")]; + tensor var_5028 = const()[name = tensor("op_5028"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_145_cast_fp16 = reshape(shape = var_5028, x = hidden_states_207_cast_fp16)[name = tensor("inputs_145_cast_fp16")]; + tensor hidden_states_209_axes_0 = const()[name = tensor("hidden_states_209_axes_0"), val = tensor([1])]; + tensor hidden_states_209_gamma_0_to_fp16 = const()[name = tensor("hidden_states_209_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647778432)))]; + tensor hidden_states_209_beta_0_to_fp16 = const()[name = tensor("hidden_states_209_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647781056)))]; + tensor var_5044_to_fp16 = const()[name = tensor("op_5044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_209_cast_fp16 = layer_norm(axes = hidden_states_209_axes_0, beta = hidden_states_209_beta_0_to_fp16, epsilon = var_5044_to_fp16, gamma = hidden_states_209_gamma_0_to_fp16, x = inputs_145_cast_fp16)[name = tensor("hidden_states_209_cast_fp16")]; + tensor var_5059 = const()[name = tensor("op_5059"), val = tensor([1, 1])]; + tensor var_5061 = const()[name = tensor("op_5061"), val = tensor([1, 1])]; + tensor q_97_pad_type_0 = const()[name = tensor("q_97_pad_type_0"), val = tensor("custom")]; + tensor q_97_pad_0 = const()[name = tensor("q_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647783680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(649012544))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_97_cast_fp16 = conv(dilations = var_5061, groups = var_4943, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_5059, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("q_97_cast_fp16")]; + tensor var_5065 = const()[name = tensor("op_5065"), val = tensor([1, 1])]; + tensor var_5067 = const()[name = tensor("op_5067"), val = tensor([1, 1])]; + tensor k_97_pad_type_0 = const()[name = tensor("k_97_pad_type_0"), val = tensor("custom")]; + tensor k_97_pad_0 = const()[name = tensor("k_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(649012736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(650241600))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_97_cast_fp16 = conv(dilations = var_5067, groups = var_4943, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_5065, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("k_97_cast_fp16")]; + tensor var_5071 = const()[name = tensor("op_5071"), val = tensor([1, 1])]; + tensor var_5073 = const()[name = tensor("op_5073"), val = tensor([1, 1])]; + tensor v_97_pad_type_0 = const()[name = tensor("v_97_pad_type_0"), val = tensor("custom")]; + tensor v_97_pad_0 = const()[name = tensor("v_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(650241792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651470656))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_97_cast_fp16 = conv(dilations = var_5073, groups = var_4943, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_5071, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_209_cast_fp16)[name = tensor("v_97_cast_fp16")]; + tensor var_5077 = const()[name = tensor("op_5077"), val = tensor([1, 20, 64, -1])]; + tensor var_5078_cast_fp16 = reshape(shape = var_5077, x = q_97_cast_fp16)[name = tensor("op_5078_cast_fp16")]; + tensor var_5079 = const()[name = tensor("op_5079"), val = tensor([1, 20, 64, -1])]; + tensor var_5080_cast_fp16 = reshape(shape = var_5079, x = k_97_cast_fp16)[name = tensor("op_5080_cast_fp16")]; + tensor var_5081 = const()[name = tensor("op_5081"), val = tensor([1, 20, 64, -1])]; + tensor var_5082_cast_fp16 = reshape(shape = var_5081, x = v_97_cast_fp16)[name = tensor("op_5082_cast_fp16")]; + tensor attn_weights_193_transpose_x_0 = const()[name = tensor("attn_weights_193_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_193_transpose_y_0 = const()[name = tensor("attn_weights_193_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5078_cast_fp16, y = var_5080_cast_fp16)[name = tensor("attn_weights_193_cast_fp16")]; + tensor var_4934_to_fp16 = const()[name = tensor("op_4934_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_195_cast_fp16")]; + tensor var_5086_cast_fp16 = softmax(axis = var_4927, x = attn_weights_195_cast_fp16)[name = tensor("op_5086_cast_fp16")]; + tensor attn_97_transpose_x_0 = const()[name = tensor("attn_97_transpose_x_0"), val = tensor(false)]; + tensor attn_97_transpose_y_0 = const()[name = tensor("attn_97_transpose_y_0"), val = tensor(true)]; + tensor attn_97_cast_fp16 = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5082_cast_fp16, y = var_5086_cast_fp16)[name = tensor("attn_97_cast_fp16")]; + tensor var_5090 = const()[name = tensor("op_5090"), val = tensor([1, 1280, 1, -1])]; + tensor input_327_cast_fp16 = reshape(shape = var_5090, x = attn_97_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor var_5095 = const()[name = tensor("op_5095"), val = tensor([1, 1])]; + tensor var_5097 = const()[name = tensor("op_5097"), val = tensor([1, 1])]; + tensor var_5099_pad_type_0 = const()[name = tensor("op_5099_pad_type_0"), val = tensor("custom")]; + tensor var_5099_pad_0 = const()[name = tensor("op_5099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651470848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652699712))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652699904)))]; + tensor var_5099_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5097, groups = var_4943, pad = var_5099_pad_0, pad_type = var_5099_pad_type_0, strides = var_5095, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_327_cast_fp16)[name = tensor("op_5099_cast_fp16")]; + tensor inputs_147_cast_fp16 = add(x = var_5099_cast_fp16, y = inputs_145_cast_fp16)[name = tensor("inputs_147_cast_fp16")]; + tensor hidden_states_211_axes_0 = const()[name = tensor("hidden_states_211_axes_0"), val = tensor([1])]; + tensor hidden_states_211_gamma_0_to_fp16 = const()[name = tensor("hidden_states_211_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652702528)))]; + tensor hidden_states_211_beta_0_to_fp16 = const()[name = tensor("hidden_states_211_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652705152)))]; + tensor var_5109_to_fp16 = const()[name = tensor("op_5109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_211_cast_fp16 = layer_norm(axes = hidden_states_211_axes_0, beta = hidden_states_211_beta_0_to_fp16, epsilon = var_5109_to_fp16, gamma = hidden_states_211_gamma_0_to_fp16, x = inputs_147_cast_fp16)[name = tensor("hidden_states_211_cast_fp16")]; + tensor var_5124 = const()[name = tensor("op_5124"), val = tensor([1, 1])]; + tensor var_5126 = const()[name = tensor("op_5126"), val = tensor([1, 1])]; + tensor q_99_pad_type_0 = const()[name = tensor("q_99_pad_type_0"), val = tensor("custom")]; + tensor q_99_pad_0 = const()[name = tensor("q_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(652707776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(653936640))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_99_cast_fp16 = conv(dilations = var_5126, groups = var_4943, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5124, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_211_cast_fp16)[name = tensor("q_99_cast_fp16")]; + tensor var_5130 = const()[name = tensor("op_5130"), val = tensor([1, 1])]; + tensor var_5132 = const()[name = tensor("op_5132"), val = tensor([1, 1])]; + tensor k_99_pad_type_0 = const()[name = tensor("k_99_pad_type_0"), val = tensor("custom")]; + tensor k_99_pad_0 = const()[name = tensor("k_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(653936832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655902976))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_99_cast_fp16 = conv(dilations = var_5132, groups = var_4943, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5130, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_99_cast_fp16")]; + tensor var_5136 = const()[name = tensor("op_5136"), val = tensor([1, 1])]; + tensor var_5138 = const()[name = tensor("op_5138"), val = tensor([1, 1])]; + tensor v_99_pad_type_0 = const()[name = tensor("v_99_pad_type_0"), val = tensor("custom")]; + tensor v_99_pad_0 = const()[name = tensor("v_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655903168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(657869312))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_99_cast_fp16 = conv(dilations = var_5138, groups = var_4943, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5136, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_99_cast_fp16")]; + tensor var_5142 = const()[name = tensor("op_5142"), val = tensor([1, 20, 64, -1])]; + tensor var_5143_cast_fp16 = reshape(shape = var_5142, x = q_99_cast_fp16)[name = tensor("op_5143_cast_fp16")]; + tensor var_5144 = const()[name = tensor("op_5144"), val = tensor([1, 20, 64, -1])]; + tensor var_5145_cast_fp16 = reshape(shape = var_5144, x = k_99_cast_fp16)[name = tensor("op_5145_cast_fp16")]; + tensor var_5146 = const()[name = tensor("op_5146"), val = tensor([1, 20, 64, -1])]; + tensor var_5147_cast_fp16 = reshape(shape = var_5146, x = v_99_cast_fp16)[name = tensor("op_5147_cast_fp16")]; + tensor attn_weights_197_transpose_x_0 = const()[name = tensor("attn_weights_197_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_197_transpose_y_0 = const()[name = tensor("attn_weights_197_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_197_cast_fp16 = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5143_cast_fp16, y = var_5145_cast_fp16)[name = tensor("attn_weights_197_cast_fp16")]; + tensor attn_weights_199_cast_fp16 = mul(x = attn_weights_197_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_199_cast_fp16")]; + tensor var_5151_cast_fp16 = softmax(axis = var_4927, x = attn_weights_199_cast_fp16)[name = tensor("op_5151_cast_fp16")]; + tensor attn_99_transpose_x_0 = const()[name = tensor("attn_99_transpose_x_0"), val = tensor(false)]; + tensor attn_99_transpose_y_0 = const()[name = tensor("attn_99_transpose_y_0"), val = tensor(true)]; + tensor attn_99_cast_fp16 = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5147_cast_fp16, y = var_5151_cast_fp16)[name = tensor("attn_99_cast_fp16")]; + tensor var_5155 = const()[name = tensor("op_5155"), val = tensor([1, 1280, 1, -1])]; + tensor input_329_cast_fp16 = reshape(shape = var_5155, x = attn_99_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor var_5160 = const()[name = tensor("op_5160"), val = tensor([1, 1])]; + tensor var_5162 = const()[name = tensor("op_5162"), val = tensor([1, 1])]; + tensor var_5164_pad_type_0 = const()[name = tensor("op_5164_pad_type_0"), val = tensor("custom")]; + tensor var_5164_pad_0 = const()[name = tensor("op_5164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(657869504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659098368))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659098560)))]; + tensor var_5164_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5162, groups = var_4943, pad = var_5164_pad_0, pad_type = var_5164_pad_type_0, strides = var_5160, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_329_cast_fp16)[name = tensor("op_5164_cast_fp16")]; + tensor inputs_149_cast_fp16 = add(x = var_5164_cast_fp16, y = inputs_147_cast_fp16)[name = tensor("inputs_149_cast_fp16")]; + tensor input_331_axes_0 = const()[name = tensor("input_331_axes_0"), val = tensor([1])]; + tensor input_331_gamma_0_to_fp16 = const()[name = tensor("input_331_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659101184)))]; + tensor input_331_beta_0_to_fp16 = const()[name = tensor("input_331_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659103808)))]; + tensor var_5174_to_fp16 = const()[name = tensor("op_5174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = input_331_beta_0_to_fp16, epsilon = var_5174_to_fp16, gamma = input_331_gamma_0_to_fp16, x = inputs_149_cast_fp16)[name = tensor("input_331_cast_fp16")]; + tensor var_5190 = const()[name = tensor("op_5190"), val = tensor([1, 1])]; + tensor var_5192 = const()[name = tensor("op_5192"), val = tensor([1, 1])]; + tensor var_5194_pad_type_0 = const()[name = tensor("op_5194_pad_type_0"), val = tensor("custom")]; + tensor var_5194_pad_0 = const()[name = tensor("op_5194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659106432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668936896))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668937088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668944832))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5194_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5192, groups = var_4943, pad = var_5194_pad_0, pad_type = var_5194_pad_type_0, strides = var_5190, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = tensor("op_5194_cast_fp16")]; + tensor var_5195_split_sizes_0 = const()[name = tensor("op_5195_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5195_axis_0 = const()[name = tensor("op_5195_axis_0"), val = tensor(1)]; + tensor var_5195_cast_fp16_0, tensor var_5195_cast_fp16_1 = split(axis = var_5195_axis_0, split_sizes = var_5195_split_sizes_0, x = var_5194_cast_fp16)[name = tensor("op_5195_cast_fp16")]; + tensor var_5197_mode_0 = const()[name = tensor("op_5197_mode_0"), val = tensor("EXACT")]; + tensor var_5197_cast_fp16 = gelu(mode = var_5197_mode_0, x = var_5195_cast_fp16_1)[name = tensor("op_5197_cast_fp16")]; + tensor input_333_cast_fp16 = mul(x = var_5195_cast_fp16_0, y = var_5197_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor var_5201 = const()[name = tensor("op_5201"), val = tensor([1, 1])]; + tensor var_5203 = const()[name = tensor("op_5203"), val = tensor([1, 1])]; + tensor var_5205_pad_type_0 = const()[name = tensor("op_5205_pad_type_0"), val = tensor("custom")]; + tensor var_5205_pad_0 = const()[name = tensor("op_5205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668945024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673860288))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673860480)))]; + tensor var_5205_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5203, groups = var_4943, pad = var_5205_pad_0, pad_type = var_5205_pad_type_0, strides = var_5201, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_333_cast_fp16)[name = tensor("op_5205_cast_fp16")]; + tensor inputs_151_cast_fp16 = add(x = var_5205_cast_fp16, y = inputs_149_cast_fp16)[name = tensor("inputs_151_cast_fp16")]; + tensor hidden_states_215_axes_0 = const()[name = tensor("hidden_states_215_axes_0"), val = tensor([1])]; + tensor hidden_states_215_gamma_0_to_fp16 = const()[name = tensor("hidden_states_215_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673863104)))]; + tensor hidden_states_215_beta_0_to_fp16 = const()[name = tensor("hidden_states_215_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673865728)))]; + tensor var_5221_to_fp16 = const()[name = tensor("op_5221_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_215_cast_fp16 = layer_norm(axes = hidden_states_215_axes_0, beta = hidden_states_215_beta_0_to_fp16, epsilon = var_5221_to_fp16, gamma = hidden_states_215_gamma_0_to_fp16, x = inputs_151_cast_fp16)[name = tensor("hidden_states_215_cast_fp16")]; + tensor var_5236 = const()[name = tensor("op_5236"), val = tensor([1, 1])]; + tensor var_5238 = const()[name = tensor("op_5238"), val = tensor([1, 1])]; + tensor q_101_pad_type_0 = const()[name = tensor("q_101_pad_type_0"), val = tensor("custom")]; + tensor q_101_pad_0 = const()[name = tensor("q_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673868352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(675097216))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_101_cast_fp16 = conv(dilations = var_5238, groups = var_4943, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5236, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_215_cast_fp16)[name = tensor("q_101_cast_fp16")]; + tensor var_5242 = const()[name = tensor("op_5242"), val = tensor([1, 1])]; + tensor var_5244 = const()[name = tensor("op_5244"), val = tensor([1, 1])]; + tensor k_101_pad_type_0 = const()[name = tensor("k_101_pad_type_0"), val = tensor("custom")]; + tensor k_101_pad_0 = const()[name = tensor("k_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(675097408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(676326272))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_101_cast_fp16 = conv(dilations = var_5244, groups = var_4943, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5242, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_215_cast_fp16)[name = tensor("k_101_cast_fp16")]; + tensor var_5248 = const()[name = tensor("op_5248"), val = tensor([1, 1])]; + tensor var_5250 = const()[name = tensor("op_5250"), val = tensor([1, 1])]; + tensor v_101_pad_type_0 = const()[name = tensor("v_101_pad_type_0"), val = tensor("custom")]; + tensor v_101_pad_0 = const()[name = tensor("v_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(676326464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677555328))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_101_cast_fp16 = conv(dilations = var_5250, groups = var_4943, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5248, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_215_cast_fp16)[name = tensor("v_101_cast_fp16")]; + tensor var_5254 = const()[name = tensor("op_5254"), val = tensor([1, 20, 64, -1])]; + tensor var_5255_cast_fp16 = reshape(shape = var_5254, x = q_101_cast_fp16)[name = tensor("op_5255_cast_fp16")]; + tensor var_5256 = const()[name = tensor("op_5256"), val = tensor([1, 20, 64, -1])]; + tensor var_5257_cast_fp16 = reshape(shape = var_5256, x = k_101_cast_fp16)[name = tensor("op_5257_cast_fp16")]; + tensor var_5258 = const()[name = tensor("op_5258"), val = tensor([1, 20, 64, -1])]; + tensor var_5259_cast_fp16 = reshape(shape = var_5258, x = v_101_cast_fp16)[name = tensor("op_5259_cast_fp16")]; + tensor attn_weights_201_transpose_x_0 = const()[name = tensor("attn_weights_201_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_201_transpose_y_0 = const()[name = tensor("attn_weights_201_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5255_cast_fp16, y = var_5257_cast_fp16)[name = tensor("attn_weights_201_cast_fp16")]; + tensor attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_203_cast_fp16")]; + tensor var_5263_cast_fp16 = softmax(axis = var_4927, x = attn_weights_203_cast_fp16)[name = tensor("op_5263_cast_fp16")]; + tensor attn_101_transpose_x_0 = const()[name = tensor("attn_101_transpose_x_0"), val = tensor(false)]; + tensor attn_101_transpose_y_0 = const()[name = tensor("attn_101_transpose_y_0"), val = tensor(true)]; + tensor attn_101_cast_fp16 = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5259_cast_fp16, y = var_5263_cast_fp16)[name = tensor("attn_101_cast_fp16")]; + tensor var_5267 = const()[name = tensor("op_5267"), val = tensor([1, 1280, 1, -1])]; + tensor input_335_cast_fp16 = reshape(shape = var_5267, x = attn_101_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor var_5272 = const()[name = tensor("op_5272"), val = tensor([1, 1])]; + tensor var_5274 = const()[name = tensor("op_5274"), val = tensor([1, 1])]; + tensor var_5276_pad_type_0 = const()[name = tensor("op_5276_pad_type_0"), val = tensor("custom")]; + tensor var_5276_pad_0 = const()[name = tensor("op_5276_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677555520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678784384))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678784576)))]; + tensor var_5276_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_5274, groups = var_4943, pad = var_5276_pad_0, pad_type = var_5276_pad_type_0, strides = var_5272, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = tensor("op_5276_cast_fp16")]; + tensor inputs_153_cast_fp16 = add(x = var_5276_cast_fp16, y = inputs_151_cast_fp16)[name = tensor("inputs_153_cast_fp16")]; + tensor hidden_states_217_axes_0 = const()[name = tensor("hidden_states_217_axes_0"), val = tensor([1])]; + tensor hidden_states_217_gamma_0_to_fp16 = const()[name = tensor("hidden_states_217_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678787200)))]; + tensor hidden_states_217_beta_0_to_fp16 = const()[name = tensor("hidden_states_217_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678789824)))]; + tensor var_5286_to_fp16 = const()[name = tensor("op_5286_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_217_cast_fp16 = layer_norm(axes = hidden_states_217_axes_0, beta = hidden_states_217_beta_0_to_fp16, epsilon = var_5286_to_fp16, gamma = hidden_states_217_gamma_0_to_fp16, x = inputs_153_cast_fp16)[name = tensor("hidden_states_217_cast_fp16")]; + tensor var_5301 = const()[name = tensor("op_5301"), val = tensor([1, 1])]; + tensor var_5303 = const()[name = tensor("op_5303"), val = tensor([1, 1])]; + tensor q_103_pad_type_0 = const()[name = tensor("q_103_pad_type_0"), val = tensor("custom")]; + tensor q_103_pad_0 = const()[name = tensor("q_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678792448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680021312))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_103_cast_fp16 = conv(dilations = var_5303, groups = var_4943, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5301, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_217_cast_fp16)[name = tensor("q_103_cast_fp16")]; + tensor var_5307 = const()[name = tensor("op_5307"), val = tensor([1, 1])]; + tensor var_5309 = const()[name = tensor("op_5309"), val = tensor([1, 1])]; + tensor k_103_pad_type_0 = const()[name = tensor("k_103_pad_type_0"), val = tensor("custom")]; + tensor k_103_pad_0 = const()[name = tensor("k_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680021504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(681987648))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_103_cast_fp16 = conv(dilations = var_5309, groups = var_4943, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5307, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_103_cast_fp16")]; + tensor var_5313 = const()[name = tensor("op_5313"), val = tensor([1, 1])]; + tensor var_5315 = const()[name = tensor("op_5315"), val = tensor([1, 1])]; + tensor v_103_pad_type_0 = const()[name = tensor("v_103_pad_type_0"), val = tensor("custom")]; + tensor v_103_pad_0 = const()[name = tensor("v_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(681987840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683953984))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_103_cast_fp16 = conv(dilations = var_5315, groups = var_4943, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5313, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_103_cast_fp16")]; + tensor var_5319 = const()[name = tensor("op_5319"), val = tensor([1, 20, 64, -1])]; + tensor var_5320_cast_fp16 = reshape(shape = var_5319, x = q_103_cast_fp16)[name = tensor("op_5320_cast_fp16")]; + tensor var_5321 = const()[name = tensor("op_5321"), val = tensor([1, 20, 64, -1])]; + tensor var_5322_cast_fp16 = reshape(shape = var_5321, x = k_103_cast_fp16)[name = tensor("op_5322_cast_fp16")]; + tensor var_5323 = const()[name = tensor("op_5323"), val = tensor([1, 20, 64, -1])]; + tensor var_5324_cast_fp16 = reshape(shape = var_5323, x = v_103_cast_fp16)[name = tensor("op_5324_cast_fp16")]; + tensor attn_weights_205_transpose_x_0 = const()[name = tensor("attn_weights_205_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_205_transpose_y_0 = const()[name = tensor("attn_weights_205_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_205_cast_fp16 = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5320_cast_fp16, y = var_5322_cast_fp16)[name = tensor("attn_weights_205_cast_fp16")]; + tensor attn_weights_207_cast_fp16 = mul(x = attn_weights_205_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_207_cast_fp16")]; + tensor var_5328_cast_fp16 = softmax(axis = var_4927, x = attn_weights_207_cast_fp16)[name = tensor("op_5328_cast_fp16")]; + tensor attn_103_transpose_x_0 = const()[name = tensor("attn_103_transpose_x_0"), val = tensor(false)]; + tensor attn_103_transpose_y_0 = const()[name = tensor("attn_103_transpose_y_0"), val = tensor(true)]; + tensor attn_103_cast_fp16 = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5324_cast_fp16, y = var_5328_cast_fp16)[name = tensor("attn_103_cast_fp16")]; + tensor var_5332 = const()[name = tensor("op_5332"), val = tensor([1, 1280, 1, -1])]; + tensor input_337_cast_fp16 = reshape(shape = var_5332, x = attn_103_cast_fp16)[name = tensor("input_337_cast_fp16")]; + tensor var_5337 = const()[name = tensor("op_5337"), val = tensor([1, 1])]; + tensor var_5339 = const()[name = tensor("op_5339"), val = tensor([1, 1])]; + tensor var_5341_pad_type_0 = const()[name = tensor("op_5341_pad_type_0"), val = tensor("custom")]; + tensor var_5341_pad_0 = const()[name = tensor("op_5341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(683954176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685183040))), name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685183232)))]; + tensor var_5341_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5339, groups = var_4943, pad = var_5341_pad_0, pad_type = var_5341_pad_type_0, strides = var_5337, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("op_5341_cast_fp16")]; + tensor inputs_155_cast_fp16 = add(x = var_5341_cast_fp16, y = inputs_153_cast_fp16)[name = tensor("inputs_155_cast_fp16")]; + tensor input_339_axes_0 = const()[name = tensor("input_339_axes_0"), val = tensor([1])]; + tensor input_339_gamma_0_to_fp16 = const()[name = tensor("input_339_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685185856)))]; + tensor input_339_beta_0_to_fp16 = const()[name = tensor("input_339_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685188480)))]; + tensor var_5351_to_fp16 = const()[name = tensor("op_5351_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = input_339_beta_0_to_fp16, epsilon = var_5351_to_fp16, gamma = input_339_gamma_0_to_fp16, x = inputs_155_cast_fp16)[name = tensor("input_339_cast_fp16")]; + tensor var_5367 = const()[name = tensor("op_5367"), val = tensor([1, 1])]; + tensor var_5369 = const()[name = tensor("op_5369"), val = tensor([1, 1])]; + tensor var_5371_pad_type_0 = const()[name = tensor("op_5371_pad_type_0"), val = tensor("custom")]; + tensor var_5371_pad_0 = const()[name = tensor("op_5371_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(685191104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695021568))), name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695021760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695029504))), name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5371_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5369, groups = var_4943, pad = var_5371_pad_0, pad_type = var_5371_pad_type_0, strides = var_5367, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_339_cast_fp16)[name = tensor("op_5371_cast_fp16")]; + tensor var_5372_split_sizes_0 = const()[name = tensor("op_5372_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5372_axis_0 = const()[name = tensor("op_5372_axis_0"), val = tensor(1)]; + tensor var_5372_cast_fp16_0, tensor var_5372_cast_fp16_1 = split(axis = var_5372_axis_0, split_sizes = var_5372_split_sizes_0, x = var_5371_cast_fp16)[name = tensor("op_5372_cast_fp16")]; + tensor var_5374_mode_0 = const()[name = tensor("op_5374_mode_0"), val = tensor("EXACT")]; + tensor var_5374_cast_fp16 = gelu(mode = var_5374_mode_0, x = var_5372_cast_fp16_1)[name = tensor("op_5374_cast_fp16")]; + tensor input_341_cast_fp16 = mul(x = var_5372_cast_fp16_0, y = var_5374_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor var_5378 = const()[name = tensor("op_5378"), val = tensor([1, 1])]; + tensor var_5380 = const()[name = tensor("op_5380"), val = tensor([1, 1])]; + tensor var_5382_pad_type_0 = const()[name = tensor("op_5382_pad_type_0"), val = tensor("custom")]; + tensor var_5382_pad_0 = const()[name = tensor("op_5382_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(695029696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699944960))), name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699945152)))]; + tensor var_5382_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5380, groups = var_4943, pad = var_5382_pad_0, pad_type = var_5382_pad_type_0, strides = var_5378, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_341_cast_fp16)[name = tensor("op_5382_cast_fp16")]; + tensor inputs_157_cast_fp16 = add(x = var_5382_cast_fp16, y = inputs_155_cast_fp16)[name = tensor("inputs_157_cast_fp16")]; + tensor hidden_states_221_axes_0 = const()[name = tensor("hidden_states_221_axes_0"), val = tensor([1])]; + tensor hidden_states_221_gamma_0_to_fp16 = const()[name = tensor("hidden_states_221_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699947776)))]; + tensor hidden_states_221_beta_0_to_fp16 = const()[name = tensor("hidden_states_221_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699950400)))]; + tensor var_5398_to_fp16 = const()[name = tensor("op_5398_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_221_cast_fp16 = layer_norm(axes = hidden_states_221_axes_0, beta = hidden_states_221_beta_0_to_fp16, epsilon = var_5398_to_fp16, gamma = hidden_states_221_gamma_0_to_fp16, x = inputs_157_cast_fp16)[name = tensor("hidden_states_221_cast_fp16")]; + tensor var_5413 = const()[name = tensor("op_5413"), val = tensor([1, 1])]; + tensor var_5415 = const()[name = tensor("op_5415"), val = tensor([1, 1])]; + tensor q_105_pad_type_0 = const()[name = tensor("q_105_pad_type_0"), val = tensor("custom")]; + tensor q_105_pad_0 = const()[name = tensor("q_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(699953024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(701181888))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_105_cast_fp16 = conv(dilations = var_5415, groups = var_4943, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5413, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_221_cast_fp16)[name = tensor("q_105_cast_fp16")]; + tensor var_5419 = const()[name = tensor("op_5419"), val = tensor([1, 1])]; + tensor var_5421 = const()[name = tensor("op_5421"), val = tensor([1, 1])]; + tensor k_105_pad_type_0 = const()[name = tensor("k_105_pad_type_0"), val = tensor("custom")]; + tensor k_105_pad_0 = const()[name = tensor("k_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(701182080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(702410944))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_105_cast_fp16 = conv(dilations = var_5421, groups = var_4943, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5419, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_221_cast_fp16)[name = tensor("k_105_cast_fp16")]; + tensor var_5425 = const()[name = tensor("op_5425"), val = tensor([1, 1])]; + tensor var_5427 = const()[name = tensor("op_5427"), val = tensor([1, 1])]; + tensor v_105_pad_type_0 = const()[name = tensor("v_105_pad_type_0"), val = tensor("custom")]; + tensor v_105_pad_0 = const()[name = tensor("v_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(702411136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703640000))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_105_cast_fp16 = conv(dilations = var_5427, groups = var_4943, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5425, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_221_cast_fp16)[name = tensor("v_105_cast_fp16")]; + tensor var_5431 = const()[name = tensor("op_5431"), val = tensor([1, 20, 64, -1])]; + tensor var_5432_cast_fp16 = reshape(shape = var_5431, x = q_105_cast_fp16)[name = tensor("op_5432_cast_fp16")]; + tensor var_5433 = const()[name = tensor("op_5433"), val = tensor([1, 20, 64, -1])]; + tensor var_5434_cast_fp16 = reshape(shape = var_5433, x = k_105_cast_fp16)[name = tensor("op_5434_cast_fp16")]; + tensor var_5435 = const()[name = tensor("op_5435"), val = tensor([1, 20, 64, -1])]; + tensor var_5436_cast_fp16 = reshape(shape = var_5435, x = v_105_cast_fp16)[name = tensor("op_5436_cast_fp16")]; + tensor attn_weights_209_transpose_x_0 = const()[name = tensor("attn_weights_209_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_209_transpose_y_0 = const()[name = tensor("attn_weights_209_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5432_cast_fp16, y = var_5434_cast_fp16)[name = tensor("attn_weights_209_cast_fp16")]; + tensor attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_211_cast_fp16")]; + tensor var_5440_cast_fp16 = softmax(axis = var_4927, x = attn_weights_211_cast_fp16)[name = tensor("op_5440_cast_fp16")]; + tensor attn_105_transpose_x_0 = const()[name = tensor("attn_105_transpose_x_0"), val = tensor(false)]; + tensor attn_105_transpose_y_0 = const()[name = tensor("attn_105_transpose_y_0"), val = tensor(true)]; + tensor attn_105_cast_fp16 = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5436_cast_fp16, y = var_5440_cast_fp16)[name = tensor("attn_105_cast_fp16")]; + tensor var_5444 = const()[name = tensor("op_5444"), val = tensor([1, 1280, 1, -1])]; + tensor input_343_cast_fp16 = reshape(shape = var_5444, x = attn_105_cast_fp16)[name = tensor("input_343_cast_fp16")]; + tensor var_5449 = const()[name = tensor("op_5449"), val = tensor([1, 1])]; + tensor var_5451 = const()[name = tensor("op_5451"), val = tensor([1, 1])]; + tensor var_5453_pad_type_0 = const()[name = tensor("op_5453_pad_type_0"), val = tensor("custom")]; + tensor var_5453_pad_0 = const()[name = tensor("op_5453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703640192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704869056))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704869248)))]; + tensor var_5453_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5451, groups = var_4943, pad = var_5453_pad_0, pad_type = var_5453_pad_type_0, strides = var_5449, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_343_cast_fp16)[name = tensor("op_5453_cast_fp16")]; + tensor inputs_159_cast_fp16 = add(x = var_5453_cast_fp16, y = inputs_157_cast_fp16)[name = tensor("inputs_159_cast_fp16")]; + tensor hidden_states_223_axes_0 = const()[name = tensor("hidden_states_223_axes_0"), val = tensor([1])]; + tensor hidden_states_223_gamma_0_to_fp16 = const()[name = tensor("hidden_states_223_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704871872)))]; + tensor hidden_states_223_beta_0_to_fp16 = const()[name = tensor("hidden_states_223_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704874496)))]; + tensor var_5463_to_fp16 = const()[name = tensor("op_5463_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_223_cast_fp16 = layer_norm(axes = hidden_states_223_axes_0, beta = hidden_states_223_beta_0_to_fp16, epsilon = var_5463_to_fp16, gamma = hidden_states_223_gamma_0_to_fp16, x = inputs_159_cast_fp16)[name = tensor("hidden_states_223_cast_fp16")]; + tensor var_5478 = const()[name = tensor("op_5478"), val = tensor([1, 1])]; + tensor var_5480 = const()[name = tensor("op_5480"), val = tensor([1, 1])]; + tensor q_107_pad_type_0 = const()[name = tensor("q_107_pad_type_0"), val = tensor("custom")]; + tensor q_107_pad_0 = const()[name = tensor("q_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704877120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706105984))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_107_cast_fp16 = conv(dilations = var_5480, groups = var_4943, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5478, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_223_cast_fp16)[name = tensor("q_107_cast_fp16")]; + tensor var_5484 = const()[name = tensor("op_5484"), val = tensor([1, 1])]; + tensor var_5486 = const()[name = tensor("op_5486"), val = tensor([1, 1])]; + tensor k_107_pad_type_0 = const()[name = tensor("k_107_pad_type_0"), val = tensor("custom")]; + tensor k_107_pad_0 = const()[name = tensor("k_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706106176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(708072320))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_107_cast_fp16 = conv(dilations = var_5486, groups = var_4943, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5484, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_107_cast_fp16")]; + tensor var_5490 = const()[name = tensor("op_5490"), val = tensor([1, 1])]; + tensor var_5492 = const()[name = tensor("op_5492"), val = tensor([1, 1])]; + tensor v_107_pad_type_0 = const()[name = tensor("v_107_pad_type_0"), val = tensor("custom")]; + tensor v_107_pad_0 = const()[name = tensor("v_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(708072512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710038656))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_107_cast_fp16 = conv(dilations = var_5492, groups = var_4943, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5490, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_107_cast_fp16")]; + tensor var_5496 = const()[name = tensor("op_5496"), val = tensor([1, 20, 64, -1])]; + tensor var_5497_cast_fp16 = reshape(shape = var_5496, x = q_107_cast_fp16)[name = tensor("op_5497_cast_fp16")]; + tensor var_5498 = const()[name = tensor("op_5498"), val = tensor([1, 20, 64, -1])]; + tensor var_5499_cast_fp16 = reshape(shape = var_5498, x = k_107_cast_fp16)[name = tensor("op_5499_cast_fp16")]; + tensor var_5500 = const()[name = tensor("op_5500"), val = tensor([1, 20, 64, -1])]; + tensor var_5501_cast_fp16 = reshape(shape = var_5500, x = v_107_cast_fp16)[name = tensor("op_5501_cast_fp16")]; + tensor attn_weights_213_transpose_x_0 = const()[name = tensor("attn_weights_213_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_213_transpose_y_0 = const()[name = tensor("attn_weights_213_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_213_cast_fp16 = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5497_cast_fp16, y = var_5499_cast_fp16)[name = tensor("attn_weights_213_cast_fp16")]; + tensor attn_weights_215_cast_fp16 = mul(x = attn_weights_213_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_215_cast_fp16")]; + tensor var_5505_cast_fp16 = softmax(axis = var_4927, x = attn_weights_215_cast_fp16)[name = tensor("op_5505_cast_fp16")]; + tensor attn_107_transpose_x_0 = const()[name = tensor("attn_107_transpose_x_0"), val = tensor(false)]; + tensor attn_107_transpose_y_0 = const()[name = tensor("attn_107_transpose_y_0"), val = tensor(true)]; + tensor attn_107_cast_fp16 = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5501_cast_fp16, y = var_5505_cast_fp16)[name = tensor("attn_107_cast_fp16")]; + tensor var_5509 = const()[name = tensor("op_5509"), val = tensor([1, 1280, 1, -1])]; + tensor input_345_cast_fp16 = reshape(shape = var_5509, x = attn_107_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor var_5514 = const()[name = tensor("op_5514"), val = tensor([1, 1])]; + tensor var_5516 = const()[name = tensor("op_5516"), val = tensor([1, 1])]; + tensor var_5518_pad_type_0 = const()[name = tensor("op_5518_pad_type_0"), val = tensor("custom")]; + tensor var_5518_pad_0 = const()[name = tensor("op_5518_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(710038848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711267712))), name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711267904)))]; + tensor var_5518_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_5516, groups = var_4943, pad = var_5518_pad_0, pad_type = var_5518_pad_type_0, strides = var_5514, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = tensor("op_5518_cast_fp16")]; + tensor inputs_161_cast_fp16 = add(x = var_5518_cast_fp16, y = inputs_159_cast_fp16)[name = tensor("inputs_161_cast_fp16")]; + tensor input_347_axes_0 = const()[name = tensor("input_347_axes_0"), val = tensor([1])]; + tensor input_347_gamma_0_to_fp16 = const()[name = tensor("input_347_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711270528)))]; + tensor input_347_beta_0_to_fp16 = const()[name = tensor("input_347_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711273152)))]; + tensor var_5528_to_fp16 = const()[name = tensor("op_5528_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_347_cast_fp16 = layer_norm(axes = input_347_axes_0, beta = input_347_beta_0_to_fp16, epsilon = var_5528_to_fp16, gamma = input_347_gamma_0_to_fp16, x = inputs_161_cast_fp16)[name = tensor("input_347_cast_fp16")]; + tensor var_5544 = const()[name = tensor("op_5544"), val = tensor([1, 1])]; + tensor var_5546 = const()[name = tensor("op_5546"), val = tensor([1, 1])]; + tensor var_5548_pad_type_0 = const()[name = tensor("op_5548_pad_type_0"), val = tensor("custom")]; + tensor var_5548_pad_0 = const()[name = tensor("op_5548_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711275776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721106240))), name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721106432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721114176))), name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5548_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5546, groups = var_4943, pad = var_5548_pad_0, pad_type = var_5548_pad_type_0, strides = var_5544, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_347_cast_fp16)[name = tensor("op_5548_cast_fp16")]; + tensor var_5549_split_sizes_0 = const()[name = tensor("op_5549_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5549_axis_0 = const()[name = tensor("op_5549_axis_0"), val = tensor(1)]; + tensor var_5549_cast_fp16_0, tensor var_5549_cast_fp16_1 = split(axis = var_5549_axis_0, split_sizes = var_5549_split_sizes_0, x = var_5548_cast_fp16)[name = tensor("op_5549_cast_fp16")]; + tensor var_5551_mode_0 = const()[name = tensor("op_5551_mode_0"), val = tensor("EXACT")]; + tensor var_5551_cast_fp16 = gelu(mode = var_5551_mode_0, x = var_5549_cast_fp16_1)[name = tensor("op_5551_cast_fp16")]; + tensor input_349_cast_fp16 = mul(x = var_5549_cast_fp16_0, y = var_5551_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor var_5555 = const()[name = tensor("op_5555"), val = tensor([1, 1])]; + tensor var_5557 = const()[name = tensor("op_5557"), val = tensor([1, 1])]; + tensor var_5559_pad_type_0 = const()[name = tensor("op_5559_pad_type_0"), val = tensor("custom")]; + tensor var_5559_pad_0 = const()[name = tensor("op_5559_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(721114368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726029632))), name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726029824)))]; + tensor var_5559_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_5557, groups = var_4943, pad = var_5559_pad_0, pad_type = var_5559_pad_type_0, strides = var_5555, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = tensor("op_5559_cast_fp16")]; + tensor inputs_163_cast_fp16 = add(x = var_5559_cast_fp16, y = inputs_161_cast_fp16)[name = tensor("inputs_163_cast_fp16")]; + tensor hidden_states_227_axes_0 = const()[name = tensor("hidden_states_227_axes_0"), val = tensor([1])]; + tensor hidden_states_227_gamma_0_to_fp16 = const()[name = tensor("hidden_states_227_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726032448)))]; + tensor hidden_states_227_beta_0_to_fp16 = const()[name = tensor("hidden_states_227_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726035072)))]; + tensor var_5575_to_fp16 = const()[name = tensor("op_5575_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_227_cast_fp16 = layer_norm(axes = hidden_states_227_axes_0, beta = hidden_states_227_beta_0_to_fp16, epsilon = var_5575_to_fp16, gamma = hidden_states_227_gamma_0_to_fp16, x = inputs_163_cast_fp16)[name = tensor("hidden_states_227_cast_fp16")]; + tensor var_5590 = const()[name = tensor("op_5590"), val = tensor([1, 1])]; + tensor var_5592 = const()[name = tensor("op_5592"), val = tensor([1, 1])]; + tensor q_109_pad_type_0 = const()[name = tensor("q_109_pad_type_0"), val = tensor("custom")]; + tensor q_109_pad_0 = const()[name = tensor("q_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726037696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(727266560))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_109_cast_fp16 = conv(dilations = var_5592, groups = var_4943, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_5590, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("q_109_cast_fp16")]; + tensor var_5596 = const()[name = tensor("op_5596"), val = tensor([1, 1])]; + tensor var_5598 = const()[name = tensor("op_5598"), val = tensor([1, 1])]; + tensor k_109_pad_type_0 = const()[name = tensor("k_109_pad_type_0"), val = tensor("custom")]; + tensor k_109_pad_0 = const()[name = tensor("k_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(727266752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728495616))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_109_cast_fp16 = conv(dilations = var_5598, groups = var_4943, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_5596, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("k_109_cast_fp16")]; + tensor var_5602 = const()[name = tensor("op_5602"), val = tensor([1, 1])]; + tensor var_5604 = const()[name = tensor("op_5604"), val = tensor([1, 1])]; + tensor v_109_pad_type_0 = const()[name = tensor("v_109_pad_type_0"), val = tensor("custom")]; + tensor v_109_pad_0 = const()[name = tensor("v_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728495808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(729724672))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_109_cast_fp16 = conv(dilations = var_5604, groups = var_4943, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_5602, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast_fp16)[name = tensor("v_109_cast_fp16")]; + tensor var_5608 = const()[name = tensor("op_5608"), val = tensor([1, 20, 64, -1])]; + tensor var_5609_cast_fp16 = reshape(shape = var_5608, x = q_109_cast_fp16)[name = tensor("op_5609_cast_fp16")]; + tensor var_5610 = const()[name = tensor("op_5610"), val = tensor([1, 20, 64, -1])]; + tensor var_5611_cast_fp16 = reshape(shape = var_5610, x = k_109_cast_fp16)[name = tensor("op_5611_cast_fp16")]; + tensor var_5612 = const()[name = tensor("op_5612"), val = tensor([1, 20, 64, -1])]; + tensor var_5613_cast_fp16 = reshape(shape = var_5612, x = v_109_cast_fp16)[name = tensor("op_5613_cast_fp16")]; + tensor attn_weights_217_transpose_x_0 = const()[name = tensor("attn_weights_217_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_217_transpose_y_0 = const()[name = tensor("attn_weights_217_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5609_cast_fp16, y = var_5611_cast_fp16)[name = tensor("attn_weights_217_cast_fp16")]; + tensor attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_219_cast_fp16")]; + tensor var_5617_cast_fp16 = softmax(axis = var_4927, x = attn_weights_219_cast_fp16)[name = tensor("op_5617_cast_fp16")]; + tensor attn_109_transpose_x_0 = const()[name = tensor("attn_109_transpose_x_0"), val = tensor(false)]; + tensor attn_109_transpose_y_0 = const()[name = tensor("attn_109_transpose_y_0"), val = tensor(true)]; + tensor attn_109_cast_fp16 = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_5613_cast_fp16, y = var_5617_cast_fp16)[name = tensor("attn_109_cast_fp16")]; + tensor var_5621 = const()[name = tensor("op_5621"), val = tensor([1, 1280, 1, -1])]; + tensor input_351_cast_fp16 = reshape(shape = var_5621, x = attn_109_cast_fp16)[name = tensor("input_351_cast_fp16")]; + tensor var_5626 = const()[name = tensor("op_5626"), val = tensor([1, 1])]; + tensor var_5628 = const()[name = tensor("op_5628"), val = tensor([1, 1])]; + tensor var_5630_pad_type_0 = const()[name = tensor("op_5630_pad_type_0"), val = tensor("custom")]; + tensor var_5630_pad_0 = const()[name = tensor("op_5630_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(729724864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730953728))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730953920)))]; + tensor var_5630_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_5628, groups = var_4943, pad = var_5630_pad_0, pad_type = var_5630_pad_type_0, strides = var_5626, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = tensor("op_5630_cast_fp16")]; + tensor inputs_165_cast_fp16 = add(x = var_5630_cast_fp16, y = inputs_163_cast_fp16)[name = tensor("inputs_165_cast_fp16")]; + tensor hidden_states_229_axes_0 = const()[name = tensor("hidden_states_229_axes_0"), val = tensor([1])]; + tensor hidden_states_229_gamma_0_to_fp16 = const()[name = tensor("hidden_states_229_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730956544)))]; + tensor hidden_states_229_beta_0_to_fp16 = const()[name = tensor("hidden_states_229_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730959168)))]; + tensor var_5640_to_fp16 = const()[name = tensor("op_5640_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_229_cast_fp16 = layer_norm(axes = hidden_states_229_axes_0, beta = hidden_states_229_beta_0_to_fp16, epsilon = var_5640_to_fp16, gamma = hidden_states_229_gamma_0_to_fp16, x = inputs_165_cast_fp16)[name = tensor("hidden_states_229_cast_fp16")]; + tensor var_5655 = const()[name = tensor("op_5655"), val = tensor([1, 1])]; + tensor var_5657 = const()[name = tensor("op_5657"), val = tensor([1, 1])]; + tensor q_111_pad_type_0 = const()[name = tensor("q_111_pad_type_0"), val = tensor("custom")]; + tensor q_111_pad_0 = const()[name = tensor("q_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730961792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(732190656))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_111_cast_fp16 = conv(dilations = var_5657, groups = var_4943, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_5655, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast_fp16)[name = tensor("q_111_cast_fp16")]; + tensor var_5661 = const()[name = tensor("op_5661"), val = tensor([1, 1])]; + tensor var_5663 = const()[name = tensor("op_5663"), val = tensor([1, 1])]; + tensor k_111_pad_type_0 = const()[name = tensor("k_111_pad_type_0"), val = tensor("custom")]; + tensor k_111_pad_0 = const()[name = tensor("k_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(732190848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(734156992))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_111_cast_fp16 = conv(dilations = var_5663, groups = var_4943, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_5661, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_111_cast_fp16")]; + tensor var_5667 = const()[name = tensor("op_5667"), val = tensor([1, 1])]; + tensor var_5669 = const()[name = tensor("op_5669"), val = tensor([1, 1])]; + tensor v_111_pad_type_0 = const()[name = tensor("v_111_pad_type_0"), val = tensor("custom")]; + tensor v_111_pad_0 = const()[name = tensor("v_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(734157184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736123328))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_111_cast_fp16 = conv(dilations = var_5669, groups = var_4943, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_5667, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_111_cast_fp16")]; + tensor var_5673 = const()[name = tensor("op_5673"), val = tensor([1, 20, 64, -1])]; + tensor var_5674_cast_fp16 = reshape(shape = var_5673, x = q_111_cast_fp16)[name = tensor("op_5674_cast_fp16")]; + tensor var_5675 = const()[name = tensor("op_5675"), val = tensor([1, 20, 64, -1])]; + tensor var_5676_cast_fp16 = reshape(shape = var_5675, x = k_111_cast_fp16)[name = tensor("op_5676_cast_fp16")]; + tensor var_5677 = const()[name = tensor("op_5677"), val = tensor([1, 20, 64, -1])]; + tensor var_5678_cast_fp16 = reshape(shape = var_5677, x = v_111_cast_fp16)[name = tensor("op_5678_cast_fp16")]; + tensor attn_weights_221_transpose_x_0 = const()[name = tensor("attn_weights_221_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_221_transpose_y_0 = const()[name = tensor("attn_weights_221_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_221_cast_fp16 = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_5674_cast_fp16, y = var_5676_cast_fp16)[name = tensor("attn_weights_221_cast_fp16")]; + tensor attn_weights_223_cast_fp16 = mul(x = attn_weights_221_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_223_cast_fp16")]; + tensor var_5682_cast_fp16 = softmax(axis = var_4927, x = attn_weights_223_cast_fp16)[name = tensor("op_5682_cast_fp16")]; + tensor attn_111_transpose_x_0 = const()[name = tensor("attn_111_transpose_x_0"), val = tensor(false)]; + tensor attn_111_transpose_y_0 = const()[name = tensor("attn_111_transpose_y_0"), val = tensor(true)]; + tensor attn_111_cast_fp16 = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_5678_cast_fp16, y = var_5682_cast_fp16)[name = tensor("attn_111_cast_fp16")]; + tensor var_5686 = const()[name = tensor("op_5686"), val = tensor([1, 1280, 1, -1])]; + tensor input_353_cast_fp16 = reshape(shape = var_5686, x = attn_111_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor var_5691 = const()[name = tensor("op_5691"), val = tensor([1, 1])]; + tensor var_5693 = const()[name = tensor("op_5693"), val = tensor([1, 1])]; + tensor var_5695_pad_type_0 = const()[name = tensor("op_5695_pad_type_0"), val = tensor("custom")]; + tensor var_5695_pad_0 = const()[name = tensor("op_5695_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736123520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737352384))), name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737352576)))]; + tensor var_5695_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_5693, groups = var_4943, pad = var_5695_pad_0, pad_type = var_5695_pad_type_0, strides = var_5691, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = tensor("op_5695_cast_fp16")]; + tensor inputs_167_cast_fp16 = add(x = var_5695_cast_fp16, y = inputs_165_cast_fp16)[name = tensor("inputs_167_cast_fp16")]; + tensor input_355_axes_0 = const()[name = tensor("input_355_axes_0"), val = tensor([1])]; + tensor input_355_gamma_0_to_fp16 = const()[name = tensor("input_355_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737355200)))]; + tensor input_355_beta_0_to_fp16 = const()[name = tensor("input_355_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737357824)))]; + tensor var_5705_to_fp16 = const()[name = tensor("op_5705_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_355_cast_fp16 = layer_norm(axes = input_355_axes_0, beta = input_355_beta_0_to_fp16, epsilon = var_5705_to_fp16, gamma = input_355_gamma_0_to_fp16, x = inputs_167_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor var_5721 = const()[name = tensor("op_5721"), val = tensor([1, 1])]; + tensor var_5723 = const()[name = tensor("op_5723"), val = tensor([1, 1])]; + tensor var_5725_pad_type_0 = const()[name = tensor("op_5725_pad_type_0"), val = tensor("custom")]; + tensor var_5725_pad_0 = const()[name = tensor("op_5725_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737360448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747190912))), name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747191104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747198848))), name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5725_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5723, groups = var_4943, pad = var_5725_pad_0, pad_type = var_5725_pad_type_0, strides = var_5721, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = tensor("op_5725_cast_fp16")]; + tensor var_5726_split_sizes_0 = const()[name = tensor("op_5726_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5726_axis_0 = const()[name = tensor("op_5726_axis_0"), val = tensor(1)]; + tensor var_5726_cast_fp16_0, tensor var_5726_cast_fp16_1 = split(axis = var_5726_axis_0, split_sizes = var_5726_split_sizes_0, x = var_5725_cast_fp16)[name = tensor("op_5726_cast_fp16")]; + tensor var_5728_mode_0 = const()[name = tensor("op_5728_mode_0"), val = tensor("EXACT")]; + tensor var_5728_cast_fp16 = gelu(mode = var_5728_mode_0, x = var_5726_cast_fp16_1)[name = tensor("op_5728_cast_fp16")]; + tensor input_357_cast_fp16 = mul(x = var_5726_cast_fp16_0, y = var_5728_cast_fp16)[name = tensor("input_357_cast_fp16")]; + tensor var_5732 = const()[name = tensor("op_5732"), val = tensor([1, 1])]; + tensor var_5734 = const()[name = tensor("op_5734"), val = tensor([1, 1])]; + tensor var_5736_pad_type_0 = const()[name = tensor("op_5736_pad_type_0"), val = tensor("custom")]; + tensor var_5736_pad_0 = const()[name = tensor("op_5736_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747199040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752114304))), name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752114496)))]; + tensor var_5736_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_5734, groups = var_4943, pad = var_5736_pad_0, pad_type = var_5736_pad_type_0, strides = var_5732, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_357_cast_fp16)[name = tensor("op_5736_cast_fp16")]; + tensor inputs_169_cast_fp16 = add(x = var_5736_cast_fp16, y = inputs_167_cast_fp16)[name = tensor("inputs_169_cast_fp16")]; + tensor hidden_states_233_axes_0 = const()[name = tensor("hidden_states_233_axes_0"), val = tensor([1])]; + tensor hidden_states_233_gamma_0_to_fp16 = const()[name = tensor("hidden_states_233_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752117120)))]; + tensor hidden_states_233_beta_0_to_fp16 = const()[name = tensor("hidden_states_233_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752119744)))]; + tensor var_5752_to_fp16 = const()[name = tensor("op_5752_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_233_cast_fp16 = layer_norm(axes = hidden_states_233_axes_0, beta = hidden_states_233_beta_0_to_fp16, epsilon = var_5752_to_fp16, gamma = hidden_states_233_gamma_0_to_fp16, x = inputs_169_cast_fp16)[name = tensor("hidden_states_233_cast_fp16")]; + tensor var_5767 = const()[name = tensor("op_5767"), val = tensor([1, 1])]; + tensor var_5769 = const()[name = tensor("op_5769"), val = tensor([1, 1])]; + tensor q_113_pad_type_0 = const()[name = tensor("q_113_pad_type_0"), val = tensor("custom")]; + tensor q_113_pad_0 = const()[name = tensor("q_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752122368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(753351232))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_113_cast_fp16 = conv(dilations = var_5769, groups = var_4943, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_5767, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_233_cast_fp16)[name = tensor("q_113_cast_fp16")]; + tensor var_5773 = const()[name = tensor("op_5773"), val = tensor([1, 1])]; + tensor var_5775 = const()[name = tensor("op_5775"), val = tensor([1, 1])]; + tensor k_113_pad_type_0 = const()[name = tensor("k_113_pad_type_0"), val = tensor("custom")]; + tensor k_113_pad_0 = const()[name = tensor("k_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(753351424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754580288))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_113_cast_fp16 = conv(dilations = var_5775, groups = var_4943, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_5773, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_233_cast_fp16)[name = tensor("k_113_cast_fp16")]; + tensor var_5779 = const()[name = tensor("op_5779"), val = tensor([1, 1])]; + tensor var_5781 = const()[name = tensor("op_5781"), val = tensor([1, 1])]; + tensor v_113_pad_type_0 = const()[name = tensor("v_113_pad_type_0"), val = tensor("custom")]; + tensor v_113_pad_0 = const()[name = tensor("v_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754580480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755809344))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_113_cast_fp16 = conv(dilations = var_5781, groups = var_4943, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_5779, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_233_cast_fp16)[name = tensor("v_113_cast_fp16")]; + tensor var_5785 = const()[name = tensor("op_5785"), val = tensor([1, 20, 64, -1])]; + tensor var_5786_cast_fp16 = reshape(shape = var_5785, x = q_113_cast_fp16)[name = tensor("op_5786_cast_fp16")]; + tensor var_5787 = const()[name = tensor("op_5787"), val = tensor([1, 20, 64, -1])]; + tensor var_5788_cast_fp16 = reshape(shape = var_5787, x = k_113_cast_fp16)[name = tensor("op_5788_cast_fp16")]; + tensor var_5789 = const()[name = tensor("op_5789"), val = tensor([1, 20, 64, -1])]; + tensor var_5790_cast_fp16 = reshape(shape = var_5789, x = v_113_cast_fp16)[name = tensor("op_5790_cast_fp16")]; + tensor attn_weights_225_transpose_x_0 = const()[name = tensor("attn_weights_225_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_225_transpose_y_0 = const()[name = tensor("attn_weights_225_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5786_cast_fp16, y = var_5788_cast_fp16)[name = tensor("attn_weights_225_cast_fp16")]; + tensor attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_227_cast_fp16")]; + tensor var_5794_cast_fp16 = softmax(axis = var_4927, x = attn_weights_227_cast_fp16)[name = tensor("op_5794_cast_fp16")]; + tensor attn_113_transpose_x_0 = const()[name = tensor("attn_113_transpose_x_0"), val = tensor(false)]; + tensor attn_113_transpose_y_0 = const()[name = tensor("attn_113_transpose_y_0"), val = tensor(true)]; + tensor attn_113_cast_fp16 = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_5790_cast_fp16, y = var_5794_cast_fp16)[name = tensor("attn_113_cast_fp16")]; + tensor var_5798 = const()[name = tensor("op_5798"), val = tensor([1, 1280, 1, -1])]; + tensor input_359_cast_fp16 = reshape(shape = var_5798, x = attn_113_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor var_5803 = const()[name = tensor("op_5803"), val = tensor([1, 1])]; + tensor var_5805 = const()[name = tensor("op_5805"), val = tensor([1, 1])]; + tensor var_5807_pad_type_0 = const()[name = tensor("op_5807_pad_type_0"), val = tensor("custom")]; + tensor var_5807_pad_0 = const()[name = tensor("op_5807_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(755809536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757038400))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757038592)))]; + tensor var_5807_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_5805, groups = var_4943, pad = var_5807_pad_0, pad_type = var_5807_pad_type_0, strides = var_5803, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_359_cast_fp16)[name = tensor("op_5807_cast_fp16")]; + tensor inputs_171_cast_fp16 = add(x = var_5807_cast_fp16, y = inputs_169_cast_fp16)[name = tensor("inputs_171_cast_fp16")]; + tensor hidden_states_235_axes_0 = const()[name = tensor("hidden_states_235_axes_0"), val = tensor([1])]; + tensor hidden_states_235_gamma_0_to_fp16 = const()[name = tensor("hidden_states_235_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757041216)))]; + tensor hidden_states_235_beta_0_to_fp16 = const()[name = tensor("hidden_states_235_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757043840)))]; + tensor var_5817_to_fp16 = const()[name = tensor("op_5817_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_235_cast_fp16 = layer_norm(axes = hidden_states_235_axes_0, beta = hidden_states_235_beta_0_to_fp16, epsilon = var_5817_to_fp16, gamma = hidden_states_235_gamma_0_to_fp16, x = inputs_171_cast_fp16)[name = tensor("hidden_states_235_cast_fp16")]; + tensor var_5832 = const()[name = tensor("op_5832"), val = tensor([1, 1])]; + tensor var_5834 = const()[name = tensor("op_5834"), val = tensor([1, 1])]; + tensor q_115_pad_type_0 = const()[name = tensor("q_115_pad_type_0"), val = tensor("custom")]; + tensor q_115_pad_0 = const()[name = tensor("q_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757046464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(758275328))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_115_cast_fp16 = conv(dilations = var_5834, groups = var_4943, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_5832, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_235_cast_fp16)[name = tensor("q_115_cast_fp16")]; + tensor var_5838 = const()[name = tensor("op_5838"), val = tensor([1, 1])]; + tensor var_5840 = const()[name = tensor("op_5840"), val = tensor([1, 1])]; + tensor k_115_pad_type_0 = const()[name = tensor("k_115_pad_type_0"), val = tensor("custom")]; + tensor k_115_pad_0 = const()[name = tensor("k_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(758275520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760241664))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_115_cast_fp16 = conv(dilations = var_5840, groups = var_4943, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_5838, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_115_cast_fp16")]; + tensor var_5844 = const()[name = tensor("op_5844"), val = tensor([1, 1])]; + tensor var_5846 = const()[name = tensor("op_5846"), val = tensor([1, 1])]; + tensor v_115_pad_type_0 = const()[name = tensor("v_115_pad_type_0"), val = tensor("custom")]; + tensor v_115_pad_0 = const()[name = tensor("v_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760241856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762208000))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_115_cast_fp16 = conv(dilations = var_5846, groups = var_4943, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_5844, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_115_cast_fp16")]; + tensor var_5850 = const()[name = tensor("op_5850"), val = tensor([1, 20, 64, -1])]; + tensor var_5851_cast_fp16 = reshape(shape = var_5850, x = q_115_cast_fp16)[name = tensor("op_5851_cast_fp16")]; + tensor var_5852 = const()[name = tensor("op_5852"), val = tensor([1, 20, 64, -1])]; + tensor var_5853_cast_fp16 = reshape(shape = var_5852, x = k_115_cast_fp16)[name = tensor("op_5853_cast_fp16")]; + tensor var_5854 = const()[name = tensor("op_5854"), val = tensor([1, 20, 64, -1])]; + tensor var_5855_cast_fp16 = reshape(shape = var_5854, x = v_115_cast_fp16)[name = tensor("op_5855_cast_fp16")]; + tensor attn_weights_229_transpose_x_0 = const()[name = tensor("attn_weights_229_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_229_transpose_y_0 = const()[name = tensor("attn_weights_229_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_229_cast_fp16 = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_5851_cast_fp16, y = var_5853_cast_fp16)[name = tensor("attn_weights_229_cast_fp16")]; + tensor attn_weights_231_cast_fp16 = mul(x = attn_weights_229_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_231_cast_fp16")]; + tensor var_5859_cast_fp16 = softmax(axis = var_4927, x = attn_weights_231_cast_fp16)[name = tensor("op_5859_cast_fp16")]; + tensor attn_115_transpose_x_0 = const()[name = tensor("attn_115_transpose_x_0"), val = tensor(false)]; + tensor attn_115_transpose_y_0 = const()[name = tensor("attn_115_transpose_y_0"), val = tensor(true)]; + tensor attn_115_cast_fp16 = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_5855_cast_fp16, y = var_5859_cast_fp16)[name = tensor("attn_115_cast_fp16")]; + tensor var_5863 = const()[name = tensor("op_5863"), val = tensor([1, 1280, 1, -1])]; + tensor input_361_cast_fp16 = reshape(shape = var_5863, x = attn_115_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor var_5868 = const()[name = tensor("op_5868"), val = tensor([1, 1])]; + tensor var_5870 = const()[name = tensor("op_5870"), val = tensor([1, 1])]; + tensor var_5872_pad_type_0 = const()[name = tensor("op_5872_pad_type_0"), val = tensor("custom")]; + tensor var_5872_pad_0 = const()[name = tensor("op_5872_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762208192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763437056))), name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763437248)))]; + tensor var_5872_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_5870, groups = var_4943, pad = var_5872_pad_0, pad_type = var_5872_pad_type_0, strides = var_5868, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = tensor("op_5872_cast_fp16")]; + tensor inputs_173_cast_fp16 = add(x = var_5872_cast_fp16, y = inputs_171_cast_fp16)[name = tensor("inputs_173_cast_fp16")]; + tensor input_363_axes_0 = const()[name = tensor("input_363_axes_0"), val = tensor([1])]; + tensor input_363_gamma_0_to_fp16 = const()[name = tensor("input_363_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763439872)))]; + tensor input_363_beta_0_to_fp16 = const()[name = tensor("input_363_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763442496)))]; + tensor var_5882_to_fp16 = const()[name = tensor("op_5882_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_363_cast_fp16 = layer_norm(axes = input_363_axes_0, beta = input_363_beta_0_to_fp16, epsilon = var_5882_to_fp16, gamma = input_363_gamma_0_to_fp16, x = inputs_173_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor var_5898 = const()[name = tensor("op_5898"), val = tensor([1, 1])]; + tensor var_5900 = const()[name = tensor("op_5900"), val = tensor([1, 1])]; + tensor var_5902_pad_type_0 = const()[name = tensor("op_5902_pad_type_0"), val = tensor("custom")]; + tensor var_5902_pad_0 = const()[name = tensor("op_5902_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763445120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773275584))), name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773275776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773283520))), name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_5902_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_5900, groups = var_4943, pad = var_5902_pad_0, pad_type = var_5902_pad_type_0, strides = var_5898, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_363_cast_fp16)[name = tensor("op_5902_cast_fp16")]; + tensor var_5903_split_sizes_0 = const()[name = tensor("op_5903_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5903_axis_0 = const()[name = tensor("op_5903_axis_0"), val = tensor(1)]; + tensor var_5903_cast_fp16_0, tensor var_5903_cast_fp16_1 = split(axis = var_5903_axis_0, split_sizes = var_5903_split_sizes_0, x = var_5902_cast_fp16)[name = tensor("op_5903_cast_fp16")]; + tensor var_5905_mode_0 = const()[name = tensor("op_5905_mode_0"), val = tensor("EXACT")]; + tensor var_5905_cast_fp16 = gelu(mode = var_5905_mode_0, x = var_5903_cast_fp16_1)[name = tensor("op_5905_cast_fp16")]; + tensor input_365_cast_fp16 = mul(x = var_5903_cast_fp16_0, y = var_5905_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor var_5909 = const()[name = tensor("op_5909"), val = tensor([1, 1])]; + tensor var_5911 = const()[name = tensor("op_5911"), val = tensor([1, 1])]; + tensor var_5913_pad_type_0 = const()[name = tensor("op_5913_pad_type_0"), val = tensor("custom")]; + tensor var_5913_pad_0 = const()[name = tensor("op_5913_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773283712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778198976))), name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778199168)))]; + tensor var_5913_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_5911, groups = var_4943, pad = var_5913_pad_0, pad_type = var_5913_pad_type_0, strides = var_5909, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_365_cast_fp16)[name = tensor("op_5913_cast_fp16")]; + tensor inputs_175_cast_fp16 = add(x = var_5913_cast_fp16, y = inputs_173_cast_fp16)[name = tensor("inputs_175_cast_fp16")]; + tensor hidden_states_239_axes_0 = const()[name = tensor("hidden_states_239_axes_0"), val = tensor([1])]; + tensor hidden_states_239_gamma_0_to_fp16 = const()[name = tensor("hidden_states_239_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778201792)))]; + tensor hidden_states_239_beta_0_to_fp16 = const()[name = tensor("hidden_states_239_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778204416)))]; + tensor var_5929_to_fp16 = const()[name = tensor("op_5929_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_239_cast_fp16 = layer_norm(axes = hidden_states_239_axes_0, beta = hidden_states_239_beta_0_to_fp16, epsilon = var_5929_to_fp16, gamma = hidden_states_239_gamma_0_to_fp16, x = inputs_175_cast_fp16)[name = tensor("hidden_states_239_cast_fp16")]; + tensor var_5944 = const()[name = tensor("op_5944"), val = tensor([1, 1])]; + tensor var_5946 = const()[name = tensor("op_5946"), val = tensor([1, 1])]; + tensor q_117_pad_type_0 = const()[name = tensor("q_117_pad_type_0"), val = tensor("custom")]; + tensor q_117_pad_0 = const()[name = tensor("q_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778207040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(779435904))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_117_cast_fp16 = conv(dilations = var_5946, groups = var_4943, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_5944, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_239_cast_fp16)[name = tensor("q_117_cast_fp16")]; + tensor var_5950 = const()[name = tensor("op_5950"), val = tensor([1, 1])]; + tensor var_5952 = const()[name = tensor("op_5952"), val = tensor([1, 1])]; + tensor k_117_pad_type_0 = const()[name = tensor("k_117_pad_type_0"), val = tensor("custom")]; + tensor k_117_pad_0 = const()[name = tensor("k_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(779436096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(780664960))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_117_cast_fp16 = conv(dilations = var_5952, groups = var_4943, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_5950, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_239_cast_fp16)[name = tensor("k_117_cast_fp16")]; + tensor var_5956 = const()[name = tensor("op_5956"), val = tensor([1, 1])]; + tensor var_5958 = const()[name = tensor("op_5958"), val = tensor([1, 1])]; + tensor v_117_pad_type_0 = const()[name = tensor("v_117_pad_type_0"), val = tensor("custom")]; + tensor v_117_pad_0 = const()[name = tensor("v_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(780665152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781894016))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_117_cast_fp16 = conv(dilations = var_5958, groups = var_4943, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_5956, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_239_cast_fp16)[name = tensor("v_117_cast_fp16")]; + tensor var_5962 = const()[name = tensor("op_5962"), val = tensor([1, 20, 64, -1])]; + tensor var_5963_cast_fp16 = reshape(shape = var_5962, x = q_117_cast_fp16)[name = tensor("op_5963_cast_fp16")]; + tensor var_5964 = const()[name = tensor("op_5964"), val = tensor([1, 20, 64, -1])]; + tensor var_5965_cast_fp16 = reshape(shape = var_5964, x = k_117_cast_fp16)[name = tensor("op_5965_cast_fp16")]; + tensor var_5966 = const()[name = tensor("op_5966"), val = tensor([1, 20, 64, -1])]; + tensor var_5967_cast_fp16 = reshape(shape = var_5966, x = v_117_cast_fp16)[name = tensor("op_5967_cast_fp16")]; + tensor attn_weights_233_transpose_x_0 = const()[name = tensor("attn_weights_233_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_233_transpose_y_0 = const()[name = tensor("attn_weights_233_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5963_cast_fp16, y = var_5965_cast_fp16)[name = tensor("attn_weights_233_cast_fp16")]; + tensor attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_235_cast_fp16")]; + tensor var_5971_cast_fp16 = softmax(axis = var_4927, x = attn_weights_235_cast_fp16)[name = tensor("op_5971_cast_fp16")]; + tensor attn_117_transpose_x_0 = const()[name = tensor("attn_117_transpose_x_0"), val = tensor(false)]; + tensor attn_117_transpose_y_0 = const()[name = tensor("attn_117_transpose_y_0"), val = tensor(true)]; + tensor attn_117_cast_fp16 = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_5967_cast_fp16, y = var_5971_cast_fp16)[name = tensor("attn_117_cast_fp16")]; + tensor var_5975 = const()[name = tensor("op_5975"), val = tensor([1, 1280, 1, -1])]; + tensor input_367_cast_fp16 = reshape(shape = var_5975, x = attn_117_cast_fp16)[name = tensor("input_367_cast_fp16")]; + tensor var_5980 = const()[name = tensor("op_5980"), val = tensor([1, 1])]; + tensor var_5982 = const()[name = tensor("op_5982"), val = tensor([1, 1])]; + tensor var_5984_pad_type_0 = const()[name = tensor("op_5984_pad_type_0"), val = tensor("custom")]; + tensor var_5984_pad_0 = const()[name = tensor("op_5984_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781894208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783123072))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783123264)))]; + tensor var_5984_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_5982, groups = var_4943, pad = var_5984_pad_0, pad_type = var_5984_pad_type_0, strides = var_5980, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_367_cast_fp16)[name = tensor("op_5984_cast_fp16")]; + tensor inputs_177_cast_fp16 = add(x = var_5984_cast_fp16, y = inputs_175_cast_fp16)[name = tensor("inputs_177_cast_fp16")]; + tensor hidden_states_241_axes_0 = const()[name = tensor("hidden_states_241_axes_0"), val = tensor([1])]; + tensor hidden_states_241_gamma_0_to_fp16 = const()[name = tensor("hidden_states_241_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783125888)))]; + tensor hidden_states_241_beta_0_to_fp16 = const()[name = tensor("hidden_states_241_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783128512)))]; + tensor var_5994_to_fp16 = const()[name = tensor("op_5994_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_241_cast_fp16 = layer_norm(axes = hidden_states_241_axes_0, beta = hidden_states_241_beta_0_to_fp16, epsilon = var_5994_to_fp16, gamma = hidden_states_241_gamma_0_to_fp16, x = inputs_177_cast_fp16)[name = tensor("hidden_states_241_cast_fp16")]; + tensor var_6009 = const()[name = tensor("op_6009"), val = tensor([1, 1])]; + tensor var_6011 = const()[name = tensor("op_6011"), val = tensor([1, 1])]; + tensor q_119_pad_type_0 = const()[name = tensor("q_119_pad_type_0"), val = tensor("custom")]; + tensor q_119_pad_0 = const()[name = tensor("q_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(783131136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784360000))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_119_cast_fp16 = conv(dilations = var_6011, groups = var_4943, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_6009, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_241_cast_fp16)[name = tensor("q_119_cast_fp16")]; + tensor var_6015 = const()[name = tensor("op_6015"), val = tensor([1, 1])]; + tensor var_6017 = const()[name = tensor("op_6017"), val = tensor([1, 1])]; + tensor k_119_pad_type_0 = const()[name = tensor("k_119_pad_type_0"), val = tensor("custom")]; + tensor k_119_pad_0 = const()[name = tensor("k_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784360192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786326336))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_119_cast_fp16 = conv(dilations = var_6017, groups = var_4943, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_6015, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_119_cast_fp16")]; + tensor var_6021 = const()[name = tensor("op_6021"), val = tensor([1, 1])]; + tensor var_6023 = const()[name = tensor("op_6023"), val = tensor([1, 1])]; + tensor v_119_pad_type_0 = const()[name = tensor("v_119_pad_type_0"), val = tensor("custom")]; + tensor v_119_pad_0 = const()[name = tensor("v_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(786326528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788292672))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_119_cast_fp16 = conv(dilations = var_6023, groups = var_4943, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_6021, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_119_cast_fp16")]; + tensor var_6027 = const()[name = tensor("op_6027"), val = tensor([1, 20, 64, -1])]; + tensor var_6028_cast_fp16 = reshape(shape = var_6027, x = q_119_cast_fp16)[name = tensor("op_6028_cast_fp16")]; + tensor var_6029 = const()[name = tensor("op_6029"), val = tensor([1, 20, 64, -1])]; + tensor var_6030_cast_fp16 = reshape(shape = var_6029, x = k_119_cast_fp16)[name = tensor("op_6030_cast_fp16")]; + tensor var_6031 = const()[name = tensor("op_6031"), val = tensor([1, 20, 64, -1])]; + tensor var_6032_cast_fp16 = reshape(shape = var_6031, x = v_119_cast_fp16)[name = tensor("op_6032_cast_fp16")]; + tensor attn_weights_237_transpose_x_0 = const()[name = tensor("attn_weights_237_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_237_transpose_y_0 = const()[name = tensor("attn_weights_237_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_237_cast_fp16 = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_6028_cast_fp16, y = var_6030_cast_fp16)[name = tensor("attn_weights_237_cast_fp16")]; + tensor attn_weights_239_cast_fp16 = mul(x = attn_weights_237_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_239_cast_fp16")]; + tensor var_6036_cast_fp16 = softmax(axis = var_4927, x = attn_weights_239_cast_fp16)[name = tensor("op_6036_cast_fp16")]; + tensor attn_119_transpose_x_0 = const()[name = tensor("attn_119_transpose_x_0"), val = tensor(false)]; + tensor attn_119_transpose_y_0 = const()[name = tensor("attn_119_transpose_y_0"), val = tensor(true)]; + tensor attn_119_cast_fp16 = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6032_cast_fp16, y = var_6036_cast_fp16)[name = tensor("attn_119_cast_fp16")]; + tensor var_6040 = const()[name = tensor("op_6040"), val = tensor([1, 1280, 1, -1])]; + tensor input_369_cast_fp16 = reshape(shape = var_6040, x = attn_119_cast_fp16)[name = tensor("input_369_cast_fp16")]; + tensor var_6045 = const()[name = tensor("op_6045"), val = tensor([1, 1])]; + tensor var_6047 = const()[name = tensor("op_6047"), val = tensor([1, 1])]; + tensor var_6049_pad_type_0 = const()[name = tensor("op_6049_pad_type_0"), val = tensor("custom")]; + tensor var_6049_pad_0 = const()[name = tensor("op_6049_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788292864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789521728))), name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789521920)))]; + tensor var_6049_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_6047, groups = var_4943, pad = var_6049_pad_0, pad_type = var_6049_pad_type_0, strides = var_6045, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = tensor("op_6049_cast_fp16")]; + tensor inputs_179_cast_fp16 = add(x = var_6049_cast_fp16, y = inputs_177_cast_fp16)[name = tensor("inputs_179_cast_fp16")]; + tensor input_371_axes_0 = const()[name = tensor("input_371_axes_0"), val = tensor([1])]; + tensor input_371_gamma_0_to_fp16 = const()[name = tensor("input_371_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789524544)))]; + tensor input_371_beta_0_to_fp16 = const()[name = tensor("input_371_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789527168)))]; + tensor var_6059_to_fp16 = const()[name = tensor("op_6059_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = input_371_beta_0_to_fp16, epsilon = var_6059_to_fp16, gamma = input_371_gamma_0_to_fp16, x = inputs_179_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor var_6075 = const()[name = tensor("op_6075"), val = tensor([1, 1])]; + tensor var_6077 = const()[name = tensor("op_6077"), val = tensor([1, 1])]; + tensor var_6079_pad_type_0 = const()[name = tensor("op_6079_pad_type_0"), val = tensor("custom")]; + tensor var_6079_pad_0 = const()[name = tensor("op_6079_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789529792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799360256))), name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799360448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799368192))), name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6079_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6077, groups = var_4943, pad = var_6079_pad_0, pad_type = var_6079_pad_type_0, strides = var_6075, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_371_cast_fp16)[name = tensor("op_6079_cast_fp16")]; + tensor var_6080_split_sizes_0 = const()[name = tensor("op_6080_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6080_axis_0 = const()[name = tensor("op_6080_axis_0"), val = tensor(1)]; + tensor var_6080_cast_fp16_0, tensor var_6080_cast_fp16_1 = split(axis = var_6080_axis_0, split_sizes = var_6080_split_sizes_0, x = var_6079_cast_fp16)[name = tensor("op_6080_cast_fp16")]; + tensor var_6082_mode_0 = const()[name = tensor("op_6082_mode_0"), val = tensor("EXACT")]; + tensor var_6082_cast_fp16 = gelu(mode = var_6082_mode_0, x = var_6080_cast_fp16_1)[name = tensor("op_6082_cast_fp16")]; + tensor input_373_cast_fp16 = mul(x = var_6080_cast_fp16_0, y = var_6082_cast_fp16)[name = tensor("input_373_cast_fp16")]; + tensor var_6086 = const()[name = tensor("op_6086"), val = tensor([1, 1])]; + tensor var_6088 = const()[name = tensor("op_6088"), val = tensor([1, 1])]; + tensor var_6090_pad_type_0 = const()[name = tensor("op_6090_pad_type_0"), val = tensor("custom")]; + tensor var_6090_pad_0 = const()[name = tensor("op_6090_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799368384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804283648))), name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804283840)))]; + tensor var_6090_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_6088, groups = var_4943, pad = var_6090_pad_0, pad_type = var_6090_pad_type_0, strides = var_6086, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = tensor("op_6090_cast_fp16")]; + tensor inputs_181_cast_fp16 = add(x = var_6090_cast_fp16, y = inputs_179_cast_fp16)[name = tensor("inputs_181_cast_fp16")]; + tensor hidden_states_245_axes_0 = const()[name = tensor("hidden_states_245_axes_0"), val = tensor([1])]; + tensor hidden_states_245_gamma_0_to_fp16 = const()[name = tensor("hidden_states_245_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804286464)))]; + tensor hidden_states_245_beta_0_to_fp16 = const()[name = tensor("hidden_states_245_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804289088)))]; + tensor var_6106_to_fp16 = const()[name = tensor("op_6106_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_245_cast_fp16 = layer_norm(axes = hidden_states_245_axes_0, beta = hidden_states_245_beta_0_to_fp16, epsilon = var_6106_to_fp16, gamma = hidden_states_245_gamma_0_to_fp16, x = inputs_181_cast_fp16)[name = tensor("hidden_states_245_cast_fp16")]; + tensor var_6121 = const()[name = tensor("op_6121"), val = tensor([1, 1])]; + tensor var_6123 = const()[name = tensor("op_6123"), val = tensor([1, 1])]; + tensor q_121_pad_type_0 = const()[name = tensor("q_121_pad_type_0"), val = tensor("custom")]; + tensor q_121_pad_0 = const()[name = tensor("q_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804291712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805520576))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_121_cast_fp16 = conv(dilations = var_6123, groups = var_4943, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6121, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_245_cast_fp16)[name = tensor("q_121_cast_fp16")]; + tensor var_6127 = const()[name = tensor("op_6127"), val = tensor([1, 1])]; + tensor var_6129 = const()[name = tensor("op_6129"), val = tensor([1, 1])]; + tensor k_121_pad_type_0 = const()[name = tensor("k_121_pad_type_0"), val = tensor("custom")]; + tensor k_121_pad_0 = const()[name = tensor("k_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805520768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806749632))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_121_cast_fp16 = conv(dilations = var_6129, groups = var_4943, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6127, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_245_cast_fp16)[name = tensor("k_121_cast_fp16")]; + tensor var_6133 = const()[name = tensor("op_6133"), val = tensor([1, 1])]; + tensor var_6135 = const()[name = tensor("op_6135"), val = tensor([1, 1])]; + tensor v_121_pad_type_0 = const()[name = tensor("v_121_pad_type_0"), val = tensor("custom")]; + tensor v_121_pad_0 = const()[name = tensor("v_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806749824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(807978688))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_121_cast_fp16 = conv(dilations = var_6135, groups = var_4943, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6133, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_245_cast_fp16)[name = tensor("v_121_cast_fp16")]; + tensor var_6139 = const()[name = tensor("op_6139"), val = tensor([1, 20, 64, -1])]; + tensor var_6140_cast_fp16 = reshape(shape = var_6139, x = q_121_cast_fp16)[name = tensor("op_6140_cast_fp16")]; + tensor var_6141 = const()[name = tensor("op_6141"), val = tensor([1, 20, 64, -1])]; + tensor var_6142_cast_fp16 = reshape(shape = var_6141, x = k_121_cast_fp16)[name = tensor("op_6142_cast_fp16")]; + tensor var_6143 = const()[name = tensor("op_6143"), val = tensor([1, 20, 64, -1])]; + tensor var_6144_cast_fp16 = reshape(shape = var_6143, x = v_121_cast_fp16)[name = tensor("op_6144_cast_fp16")]; + tensor attn_weights_241_transpose_x_0 = const()[name = tensor("attn_weights_241_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_241_transpose_y_0 = const()[name = tensor("attn_weights_241_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6140_cast_fp16, y = var_6142_cast_fp16)[name = tensor("attn_weights_241_cast_fp16")]; + tensor attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_243_cast_fp16")]; + tensor var_6148_cast_fp16 = softmax(axis = var_4927, x = attn_weights_243_cast_fp16)[name = tensor("op_6148_cast_fp16")]; + tensor attn_121_transpose_x_0 = const()[name = tensor("attn_121_transpose_x_0"), val = tensor(false)]; + tensor attn_121_transpose_y_0 = const()[name = tensor("attn_121_transpose_y_0"), val = tensor(true)]; + tensor attn_121_cast_fp16 = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6144_cast_fp16, y = var_6148_cast_fp16)[name = tensor("attn_121_cast_fp16")]; + tensor var_6152 = const()[name = tensor("op_6152"), val = tensor([1, 1280, 1, -1])]; + tensor input_375_cast_fp16 = reshape(shape = var_6152, x = attn_121_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor var_6157 = const()[name = tensor("op_6157"), val = tensor([1, 1])]; + tensor var_6159 = const()[name = tensor("op_6159"), val = tensor([1, 1])]; + tensor var_6161_pad_type_0 = const()[name = tensor("op_6161_pad_type_0"), val = tensor("custom")]; + tensor var_6161_pad_0 = const()[name = tensor("op_6161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(807978880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809207744))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809207936)))]; + tensor var_6161_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_6159, groups = var_4943, pad = var_6161_pad_0, pad_type = var_6161_pad_type_0, strides = var_6157, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_375_cast_fp16)[name = tensor("op_6161_cast_fp16")]; + tensor inputs_183_cast_fp16 = add(x = var_6161_cast_fp16, y = inputs_181_cast_fp16)[name = tensor("inputs_183_cast_fp16")]; + tensor hidden_states_247_axes_0 = const()[name = tensor("hidden_states_247_axes_0"), val = tensor([1])]; + tensor hidden_states_247_gamma_0_to_fp16 = const()[name = tensor("hidden_states_247_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809210560)))]; + tensor hidden_states_247_beta_0_to_fp16 = const()[name = tensor("hidden_states_247_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809213184)))]; + tensor var_6171_to_fp16 = const()[name = tensor("op_6171_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_247_cast_fp16 = layer_norm(axes = hidden_states_247_axes_0, beta = hidden_states_247_beta_0_to_fp16, epsilon = var_6171_to_fp16, gamma = hidden_states_247_gamma_0_to_fp16, x = inputs_183_cast_fp16)[name = tensor("hidden_states_247_cast_fp16")]; + tensor var_6186 = const()[name = tensor("op_6186"), val = tensor([1, 1])]; + tensor var_6188 = const()[name = tensor("op_6188"), val = tensor([1, 1])]; + tensor q_123_pad_type_0 = const()[name = tensor("q_123_pad_type_0"), val = tensor("custom")]; + tensor q_123_pad_0 = const()[name = tensor("q_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809215808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810444672))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_123_cast_fp16 = conv(dilations = var_6188, groups = var_4943, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6186, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast_fp16)[name = tensor("q_123_cast_fp16")]; + tensor var_6192 = const()[name = tensor("op_6192"), val = tensor([1, 1])]; + tensor var_6194 = const()[name = tensor("op_6194"), val = tensor([1, 1])]; + tensor k_123_pad_type_0 = const()[name = tensor("k_123_pad_type_0"), val = tensor("custom")]; + tensor k_123_pad_0 = const()[name = tensor("k_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810444864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812411008))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_123_cast_fp16 = conv(dilations = var_6194, groups = var_4943, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6192, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_123_cast_fp16")]; + tensor var_6198 = const()[name = tensor("op_6198"), val = tensor([1, 1])]; + tensor var_6200 = const()[name = tensor("op_6200"), val = tensor([1, 1])]; + tensor v_123_pad_type_0 = const()[name = tensor("v_123_pad_type_0"), val = tensor("custom")]; + tensor v_123_pad_0 = const()[name = tensor("v_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812411200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814377344))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_123_cast_fp16 = conv(dilations = var_6200, groups = var_4943, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6198, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_123_cast_fp16")]; + tensor var_6204 = const()[name = tensor("op_6204"), val = tensor([1, 20, 64, -1])]; + tensor var_6205_cast_fp16 = reshape(shape = var_6204, x = q_123_cast_fp16)[name = tensor("op_6205_cast_fp16")]; + tensor var_6206 = const()[name = tensor("op_6206"), val = tensor([1, 20, 64, -1])]; + tensor var_6207_cast_fp16 = reshape(shape = var_6206, x = k_123_cast_fp16)[name = tensor("op_6207_cast_fp16")]; + tensor var_6208 = const()[name = tensor("op_6208"), val = tensor([1, 20, 64, -1])]; + tensor var_6209_cast_fp16 = reshape(shape = var_6208, x = v_123_cast_fp16)[name = tensor("op_6209_cast_fp16")]; + tensor attn_weights_245_transpose_x_0 = const()[name = tensor("attn_weights_245_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_245_transpose_y_0 = const()[name = tensor("attn_weights_245_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_245_cast_fp16 = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6205_cast_fp16, y = var_6207_cast_fp16)[name = tensor("attn_weights_245_cast_fp16")]; + tensor attn_weights_247_cast_fp16 = mul(x = attn_weights_245_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_247_cast_fp16")]; + tensor var_6213_cast_fp16 = softmax(axis = var_4927, x = attn_weights_247_cast_fp16)[name = tensor("op_6213_cast_fp16")]; + tensor attn_123_transpose_x_0 = const()[name = tensor("attn_123_transpose_x_0"), val = tensor(false)]; + tensor attn_123_transpose_y_0 = const()[name = tensor("attn_123_transpose_y_0"), val = tensor(true)]; + tensor attn_123_cast_fp16 = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6209_cast_fp16, y = var_6213_cast_fp16)[name = tensor("attn_123_cast_fp16")]; + tensor var_6217 = const()[name = tensor("op_6217"), val = tensor([1, 1280, 1, -1])]; + tensor input_377_cast_fp16 = reshape(shape = var_6217, x = attn_123_cast_fp16)[name = tensor("input_377_cast_fp16")]; + tensor var_6222 = const()[name = tensor("op_6222"), val = tensor([1, 1])]; + tensor var_6224 = const()[name = tensor("op_6224"), val = tensor([1, 1])]; + tensor var_6226_pad_type_0 = const()[name = tensor("op_6226_pad_type_0"), val = tensor("custom")]; + tensor var_6226_pad_0 = const()[name = tensor("op_6226_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814377536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815606400))), name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815606592)))]; + tensor var_6226_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_6224, groups = var_4943, pad = var_6226_pad_0, pad_type = var_6226_pad_type_0, strides = var_6222, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = tensor("op_6226_cast_fp16")]; + tensor inputs_185_cast_fp16 = add(x = var_6226_cast_fp16, y = inputs_183_cast_fp16)[name = tensor("inputs_185_cast_fp16")]; + tensor input_379_axes_0 = const()[name = tensor("input_379_axes_0"), val = tensor([1])]; + tensor input_379_gamma_0_to_fp16 = const()[name = tensor("input_379_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815609216)))]; + tensor input_379_beta_0_to_fp16 = const()[name = tensor("input_379_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815611840)))]; + tensor var_6236_to_fp16 = const()[name = tensor("op_6236_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = input_379_beta_0_to_fp16, epsilon = var_6236_to_fp16, gamma = input_379_gamma_0_to_fp16, x = inputs_185_cast_fp16)[name = tensor("input_379_cast_fp16")]; + tensor var_6252 = const()[name = tensor("op_6252"), val = tensor([1, 1])]; + tensor var_6254 = const()[name = tensor("op_6254"), val = tensor([1, 1])]; + tensor var_6256_pad_type_0 = const()[name = tensor("op_6256_pad_type_0"), val = tensor("custom")]; + tensor var_6256_pad_0 = const()[name = tensor("op_6256_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815614464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825444928))), name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825445120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825452864))), name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6256_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6254, groups = var_4943, pad = var_6256_pad_0, pad_type = var_6256_pad_type_0, strides = var_6252, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_379_cast_fp16)[name = tensor("op_6256_cast_fp16")]; + tensor var_6257_split_sizes_0 = const()[name = tensor("op_6257_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6257_axis_0 = const()[name = tensor("op_6257_axis_0"), val = tensor(1)]; + tensor var_6257_cast_fp16_0, tensor var_6257_cast_fp16_1 = split(axis = var_6257_axis_0, split_sizes = var_6257_split_sizes_0, x = var_6256_cast_fp16)[name = tensor("op_6257_cast_fp16")]; + tensor var_6259_mode_0 = const()[name = tensor("op_6259_mode_0"), val = tensor("EXACT")]; + tensor var_6259_cast_fp16 = gelu(mode = var_6259_mode_0, x = var_6257_cast_fp16_1)[name = tensor("op_6259_cast_fp16")]; + tensor input_381_cast_fp16 = mul(x = var_6257_cast_fp16_0, y = var_6259_cast_fp16)[name = tensor("input_381_cast_fp16")]; + tensor var_6263 = const()[name = tensor("op_6263"), val = tensor([1, 1])]; + tensor var_6265 = const()[name = tensor("op_6265"), val = tensor([1, 1])]; + tensor var_6267_pad_type_0 = const()[name = tensor("op_6267_pad_type_0"), val = tensor("custom")]; + tensor var_6267_pad_0 = const()[name = tensor("op_6267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825453056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830368320))), name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830368512)))]; + tensor var_6267_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_6265, groups = var_4943, pad = var_6267_pad_0, pad_type = var_6267_pad_type_0, strides = var_6263, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = tensor("op_6267_cast_fp16")]; + tensor inputs_187_cast_fp16 = add(x = var_6267_cast_fp16, y = inputs_185_cast_fp16)[name = tensor("inputs_187_cast_fp16")]; + tensor hidden_states_251_axes_0 = const()[name = tensor("hidden_states_251_axes_0"), val = tensor([1])]; + tensor hidden_states_251_gamma_0_to_fp16 = const()[name = tensor("hidden_states_251_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830371136)))]; + tensor hidden_states_251_beta_0_to_fp16 = const()[name = tensor("hidden_states_251_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830373760)))]; + tensor var_6283_to_fp16 = const()[name = tensor("op_6283_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_251_cast_fp16 = layer_norm(axes = hidden_states_251_axes_0, beta = hidden_states_251_beta_0_to_fp16, epsilon = var_6283_to_fp16, gamma = hidden_states_251_gamma_0_to_fp16, x = inputs_187_cast_fp16)[name = tensor("hidden_states_251_cast_fp16")]; + tensor var_6298 = const()[name = tensor("op_6298"), val = tensor([1, 1])]; + tensor var_6300 = const()[name = tensor("op_6300"), val = tensor([1, 1])]; + tensor q_125_pad_type_0 = const()[name = tensor("q_125_pad_type_0"), val = tensor("custom")]; + tensor q_125_pad_0 = const()[name = tensor("q_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830376384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(831605248))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_125_cast_fp16 = conv(dilations = var_6300, groups = var_4943, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6298, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_251_cast_fp16)[name = tensor("q_125_cast_fp16")]; + tensor var_6304 = const()[name = tensor("op_6304"), val = tensor([1, 1])]; + tensor var_6306 = const()[name = tensor("op_6306"), val = tensor([1, 1])]; + tensor k_125_pad_type_0 = const()[name = tensor("k_125_pad_type_0"), val = tensor("custom")]; + tensor k_125_pad_0 = const()[name = tensor("k_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(831605440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832834304))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_125_cast_fp16 = conv(dilations = var_6306, groups = var_4943, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6304, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_251_cast_fp16)[name = tensor("k_125_cast_fp16")]; + tensor var_6310 = const()[name = tensor("op_6310"), val = tensor([1, 1])]; + tensor var_6312 = const()[name = tensor("op_6312"), val = tensor([1, 1])]; + tensor v_125_pad_type_0 = const()[name = tensor("v_125_pad_type_0"), val = tensor("custom")]; + tensor v_125_pad_0 = const()[name = tensor("v_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832834496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834063360))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_125_cast_fp16 = conv(dilations = var_6312, groups = var_4943, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6310, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_251_cast_fp16)[name = tensor("v_125_cast_fp16")]; + tensor var_6316 = const()[name = tensor("op_6316"), val = tensor([1, 20, 64, -1])]; + tensor var_6317_cast_fp16 = reshape(shape = var_6316, x = q_125_cast_fp16)[name = tensor("op_6317_cast_fp16")]; + tensor var_6318 = const()[name = tensor("op_6318"), val = tensor([1, 20, 64, -1])]; + tensor var_6319_cast_fp16 = reshape(shape = var_6318, x = k_125_cast_fp16)[name = tensor("op_6319_cast_fp16")]; + tensor var_6320 = const()[name = tensor("op_6320"), val = tensor([1, 20, 64, -1])]; + tensor var_6321_cast_fp16 = reshape(shape = var_6320, x = v_125_cast_fp16)[name = tensor("op_6321_cast_fp16")]; + tensor attn_weights_249_transpose_x_0 = const()[name = tensor("attn_weights_249_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_249_transpose_y_0 = const()[name = tensor("attn_weights_249_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6317_cast_fp16, y = var_6319_cast_fp16)[name = tensor("attn_weights_249_cast_fp16")]; + tensor attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_251_cast_fp16")]; + tensor var_6325_cast_fp16 = softmax(axis = var_4927, x = attn_weights_251_cast_fp16)[name = tensor("op_6325_cast_fp16")]; + tensor attn_125_transpose_x_0 = const()[name = tensor("attn_125_transpose_x_0"), val = tensor(false)]; + tensor attn_125_transpose_y_0 = const()[name = tensor("attn_125_transpose_y_0"), val = tensor(true)]; + tensor attn_125_cast_fp16 = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6321_cast_fp16, y = var_6325_cast_fp16)[name = tensor("attn_125_cast_fp16")]; + tensor var_6329 = const()[name = tensor("op_6329"), val = tensor([1, 1280, 1, -1])]; + tensor input_383_cast_fp16 = reshape(shape = var_6329, x = attn_125_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor var_6334 = const()[name = tensor("op_6334"), val = tensor([1, 1])]; + tensor var_6336 = const()[name = tensor("op_6336"), val = tensor([1, 1])]; + tensor var_6338_pad_type_0 = const()[name = tensor("op_6338_pad_type_0"), val = tensor("custom")]; + tensor var_6338_pad_0 = const()[name = tensor("op_6338_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834063552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835292416))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835292608)))]; + tensor var_6338_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_6336, groups = var_4943, pad = var_6338_pad_0, pad_type = var_6338_pad_type_0, strides = var_6334, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_383_cast_fp16)[name = tensor("op_6338_cast_fp16")]; + tensor inputs_189_cast_fp16 = add(x = var_6338_cast_fp16, y = inputs_187_cast_fp16)[name = tensor("inputs_189_cast_fp16")]; + tensor hidden_states_253_axes_0 = const()[name = tensor("hidden_states_253_axes_0"), val = tensor([1])]; + tensor hidden_states_253_gamma_0_to_fp16 = const()[name = tensor("hidden_states_253_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835295232)))]; + tensor hidden_states_253_beta_0_to_fp16 = const()[name = tensor("hidden_states_253_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835297856)))]; + tensor var_6348_to_fp16 = const()[name = tensor("op_6348_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_253_cast_fp16 = layer_norm(axes = hidden_states_253_axes_0, beta = hidden_states_253_beta_0_to_fp16, epsilon = var_6348_to_fp16, gamma = hidden_states_253_gamma_0_to_fp16, x = inputs_189_cast_fp16)[name = tensor("hidden_states_253_cast_fp16")]; + tensor var_6363 = const()[name = tensor("op_6363"), val = tensor([1, 1])]; + tensor var_6365 = const()[name = tensor("op_6365"), val = tensor([1, 1])]; + tensor q_127_pad_type_0 = const()[name = tensor("q_127_pad_type_0"), val = tensor("custom")]; + tensor q_127_pad_0 = const()[name = tensor("q_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835300480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836529344))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_127_cast_fp16 = conv(dilations = var_6365, groups = var_4943, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6363, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_253_cast_fp16)[name = tensor("q_127_cast_fp16")]; + tensor var_6369 = const()[name = tensor("op_6369"), val = tensor([1, 1])]; + tensor var_6371 = const()[name = tensor("op_6371"), val = tensor([1, 1])]; + tensor k_127_pad_type_0 = const()[name = tensor("k_127_pad_type_0"), val = tensor("custom")]; + tensor k_127_pad_0 = const()[name = tensor("k_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836529536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838495680))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_127_cast_fp16 = conv(dilations = var_6371, groups = var_4943, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6369, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_127_cast_fp16")]; + tensor var_6375 = const()[name = tensor("op_6375"), val = tensor([1, 1])]; + tensor var_6377 = const()[name = tensor("op_6377"), val = tensor([1, 1])]; + tensor v_127_pad_type_0 = const()[name = tensor("v_127_pad_type_0"), val = tensor("custom")]; + tensor v_127_pad_0 = const()[name = tensor("v_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838495872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(840462016))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_127_cast_fp16 = conv(dilations = var_6377, groups = var_4943, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6375, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_127_cast_fp16")]; + tensor var_6381 = const()[name = tensor("op_6381"), val = tensor([1, 20, 64, -1])]; + tensor var_6382_cast_fp16 = reshape(shape = var_6381, x = q_127_cast_fp16)[name = tensor("op_6382_cast_fp16")]; + tensor var_6383 = const()[name = tensor("op_6383"), val = tensor([1, 20, 64, -1])]; + tensor var_6384_cast_fp16 = reshape(shape = var_6383, x = k_127_cast_fp16)[name = tensor("op_6384_cast_fp16")]; + tensor var_6385 = const()[name = tensor("op_6385"), val = tensor([1, 20, 64, -1])]; + tensor var_6386_cast_fp16 = reshape(shape = var_6385, x = v_127_cast_fp16)[name = tensor("op_6386_cast_fp16")]; + tensor attn_weights_253_transpose_x_0 = const()[name = tensor("attn_weights_253_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_253_transpose_y_0 = const()[name = tensor("attn_weights_253_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_253_cast_fp16 = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6382_cast_fp16, y = var_6384_cast_fp16)[name = tensor("attn_weights_253_cast_fp16")]; + tensor attn_weights_255_cast_fp16 = mul(x = attn_weights_253_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_255_cast_fp16")]; + tensor var_6390_cast_fp16 = softmax(axis = var_4927, x = attn_weights_255_cast_fp16)[name = tensor("op_6390_cast_fp16")]; + tensor attn_127_transpose_x_0 = const()[name = tensor("attn_127_transpose_x_0"), val = tensor(false)]; + tensor attn_127_transpose_y_0 = const()[name = tensor("attn_127_transpose_y_0"), val = tensor(true)]; + tensor attn_127_cast_fp16 = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6386_cast_fp16, y = var_6390_cast_fp16)[name = tensor("attn_127_cast_fp16")]; + tensor var_6394 = const()[name = tensor("op_6394"), val = tensor([1, 1280, 1, -1])]; + tensor input_385_cast_fp16 = reshape(shape = var_6394, x = attn_127_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor var_6399 = const()[name = tensor("op_6399"), val = tensor([1, 1])]; + tensor var_6401 = const()[name = tensor("op_6401"), val = tensor([1, 1])]; + tensor var_6403_pad_type_0 = const()[name = tensor("op_6403_pad_type_0"), val = tensor("custom")]; + tensor var_6403_pad_0 = const()[name = tensor("op_6403_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(840462208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841691072))), name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841691264)))]; + tensor var_6403_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_6401, groups = var_4943, pad = var_6403_pad_0, pad_type = var_6403_pad_type_0, strides = var_6399, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_385_cast_fp16)[name = tensor("op_6403_cast_fp16")]; + tensor inputs_191_cast_fp16 = add(x = var_6403_cast_fp16, y = inputs_189_cast_fp16)[name = tensor("inputs_191_cast_fp16")]; + tensor input_387_axes_0 = const()[name = tensor("input_387_axes_0"), val = tensor([1])]; + tensor input_387_gamma_0_to_fp16 = const()[name = tensor("input_387_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841693888)))]; + tensor input_387_beta_0_to_fp16 = const()[name = tensor("input_387_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841696512)))]; + tensor var_6413_to_fp16 = const()[name = tensor("op_6413_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_387_cast_fp16 = layer_norm(axes = input_387_axes_0, beta = input_387_beta_0_to_fp16, epsilon = var_6413_to_fp16, gamma = input_387_gamma_0_to_fp16, x = inputs_191_cast_fp16)[name = tensor("input_387_cast_fp16")]; + tensor var_6429 = const()[name = tensor("op_6429"), val = tensor([1, 1])]; + tensor var_6431 = const()[name = tensor("op_6431"), val = tensor([1, 1])]; + tensor var_6433_pad_type_0 = const()[name = tensor("op_6433_pad_type_0"), val = tensor("custom")]; + tensor var_6433_pad_0 = const()[name = tensor("op_6433_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841699136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851529600))), name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851529792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851537536))), name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6433_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6431, groups = var_4943, pad = var_6433_pad_0, pad_type = var_6433_pad_type_0, strides = var_6429, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_387_cast_fp16)[name = tensor("op_6433_cast_fp16")]; + tensor var_6434_split_sizes_0 = const()[name = tensor("op_6434_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6434_axis_0 = const()[name = tensor("op_6434_axis_0"), val = tensor(1)]; + tensor var_6434_cast_fp16_0, tensor var_6434_cast_fp16_1 = split(axis = var_6434_axis_0, split_sizes = var_6434_split_sizes_0, x = var_6433_cast_fp16)[name = tensor("op_6434_cast_fp16")]; + tensor var_6436_mode_0 = const()[name = tensor("op_6436_mode_0"), val = tensor("EXACT")]; + tensor var_6436_cast_fp16 = gelu(mode = var_6436_mode_0, x = var_6434_cast_fp16_1)[name = tensor("op_6436_cast_fp16")]; + tensor input_389_cast_fp16 = mul(x = var_6434_cast_fp16_0, y = var_6436_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor var_6440 = const()[name = tensor("op_6440"), val = tensor([1, 1])]; + tensor var_6442 = const()[name = tensor("op_6442"), val = tensor([1, 1])]; + tensor var_6444_pad_type_0 = const()[name = tensor("op_6444_pad_type_0"), val = tensor("custom")]; + tensor var_6444_pad_0 = const()[name = tensor("op_6444_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(851537728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856452992))), name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856453184)))]; + tensor var_6444_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_6442, groups = var_4943, pad = var_6444_pad_0, pad_type = var_6444_pad_type_0, strides = var_6440, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = tensor("op_6444_cast_fp16")]; + tensor inputs_193_cast_fp16 = add(x = var_6444_cast_fp16, y = inputs_191_cast_fp16)[name = tensor("inputs_193_cast_fp16")]; + tensor hidden_states_257_axes_0 = const()[name = tensor("hidden_states_257_axes_0"), val = tensor([1])]; + tensor hidden_states_257_gamma_0_to_fp16 = const()[name = tensor("hidden_states_257_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856455808)))]; + tensor hidden_states_257_beta_0_to_fp16 = const()[name = tensor("hidden_states_257_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856458432)))]; + tensor var_6460_to_fp16 = const()[name = tensor("op_6460_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_257_cast_fp16 = layer_norm(axes = hidden_states_257_axes_0, beta = hidden_states_257_beta_0_to_fp16, epsilon = var_6460_to_fp16, gamma = hidden_states_257_gamma_0_to_fp16, x = inputs_193_cast_fp16)[name = tensor("hidden_states_257_cast_fp16")]; + tensor var_6475 = const()[name = tensor("op_6475"), val = tensor([1, 1])]; + tensor var_6477 = const()[name = tensor("op_6477"), val = tensor([1, 1])]; + tensor q_129_pad_type_0 = const()[name = tensor("q_129_pad_type_0"), val = tensor("custom")]; + tensor q_129_pad_0 = const()[name = tensor("q_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856461056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(857689920))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_129_cast_fp16 = conv(dilations = var_6477, groups = var_4943, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_6475, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_257_cast_fp16)[name = tensor("q_129_cast_fp16")]; + tensor var_6481 = const()[name = tensor("op_6481"), val = tensor([1, 1])]; + tensor var_6483 = const()[name = tensor("op_6483"), val = tensor([1, 1])]; + tensor k_129_pad_type_0 = const()[name = tensor("k_129_pad_type_0"), val = tensor("custom")]; + tensor k_129_pad_0 = const()[name = tensor("k_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(857690112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858918976))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_129_cast_fp16 = conv(dilations = var_6483, groups = var_4943, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_6481, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_257_cast_fp16)[name = tensor("k_129_cast_fp16")]; + tensor var_6487 = const()[name = tensor("op_6487"), val = tensor([1, 1])]; + tensor var_6489 = const()[name = tensor("op_6489"), val = tensor([1, 1])]; + tensor v_129_pad_type_0 = const()[name = tensor("v_129_pad_type_0"), val = tensor("custom")]; + tensor v_129_pad_0 = const()[name = tensor("v_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858919168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860148032))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_129_cast_fp16 = conv(dilations = var_6489, groups = var_4943, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_6487, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_257_cast_fp16)[name = tensor("v_129_cast_fp16")]; + tensor var_6493 = const()[name = tensor("op_6493"), val = tensor([1, 20, 64, -1])]; + tensor var_6494_cast_fp16 = reshape(shape = var_6493, x = q_129_cast_fp16)[name = tensor("op_6494_cast_fp16")]; + tensor var_6495 = const()[name = tensor("op_6495"), val = tensor([1, 20, 64, -1])]; + tensor var_6496_cast_fp16 = reshape(shape = var_6495, x = k_129_cast_fp16)[name = tensor("op_6496_cast_fp16")]; + tensor var_6497 = const()[name = tensor("op_6497"), val = tensor([1, 20, 64, -1])]; + tensor var_6498_cast_fp16 = reshape(shape = var_6497, x = v_129_cast_fp16)[name = tensor("op_6498_cast_fp16")]; + tensor attn_weights_257_transpose_x_0 = const()[name = tensor("attn_weights_257_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_257_transpose_y_0 = const()[name = tensor("attn_weights_257_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6494_cast_fp16, y = var_6496_cast_fp16)[name = tensor("attn_weights_257_cast_fp16")]; + tensor attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_259_cast_fp16")]; + tensor var_6502_cast_fp16 = softmax(axis = var_4927, x = attn_weights_259_cast_fp16)[name = tensor("op_6502_cast_fp16")]; + tensor attn_129_transpose_x_0 = const()[name = tensor("attn_129_transpose_x_0"), val = tensor(false)]; + tensor attn_129_transpose_y_0 = const()[name = tensor("attn_129_transpose_y_0"), val = tensor(true)]; + tensor attn_129_cast_fp16 = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_6498_cast_fp16, y = var_6502_cast_fp16)[name = tensor("attn_129_cast_fp16")]; + tensor var_6506 = const()[name = tensor("op_6506"), val = tensor([1, 1280, 1, -1])]; + tensor input_391_cast_fp16 = reshape(shape = var_6506, x = attn_129_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor var_6511 = const()[name = tensor("op_6511"), val = tensor([1, 1])]; + tensor var_6513 = const()[name = tensor("op_6513"), val = tensor([1, 1])]; + tensor var_6515_pad_type_0 = const()[name = tensor("op_6515_pad_type_0"), val = tensor("custom")]; + tensor var_6515_pad_0 = const()[name = tensor("op_6515_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860148224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861377088))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861377280)))]; + tensor var_6515_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_6513, groups = var_4943, pad = var_6515_pad_0, pad_type = var_6515_pad_type_0, strides = var_6511, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = tensor("op_6515_cast_fp16")]; + tensor inputs_195_cast_fp16 = add(x = var_6515_cast_fp16, y = inputs_193_cast_fp16)[name = tensor("inputs_195_cast_fp16")]; + tensor hidden_states_259_axes_0 = const()[name = tensor("hidden_states_259_axes_0"), val = tensor([1])]; + tensor hidden_states_259_gamma_0_to_fp16 = const()[name = tensor("hidden_states_259_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861379904)))]; + tensor hidden_states_259_beta_0_to_fp16 = const()[name = tensor("hidden_states_259_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861382528)))]; + tensor var_6525_to_fp16 = const()[name = tensor("op_6525_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_259_cast_fp16 = layer_norm(axes = hidden_states_259_axes_0, beta = hidden_states_259_beta_0_to_fp16, epsilon = var_6525_to_fp16, gamma = hidden_states_259_gamma_0_to_fp16, x = inputs_195_cast_fp16)[name = tensor("hidden_states_259_cast_fp16")]; + tensor var_6540 = const()[name = tensor("op_6540"), val = tensor([1, 1])]; + tensor var_6542 = const()[name = tensor("op_6542"), val = tensor([1, 1])]; + tensor q_131_pad_type_0 = const()[name = tensor("q_131_pad_type_0"), val = tensor("custom")]; + tensor q_131_pad_0 = const()[name = tensor("q_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861385152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(862614016))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_131_cast_fp16 = conv(dilations = var_6542, groups = var_4943, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_6540, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_259_cast_fp16)[name = tensor("q_131_cast_fp16")]; + tensor var_6546 = const()[name = tensor("op_6546"), val = tensor([1, 1])]; + tensor var_6548 = const()[name = tensor("op_6548"), val = tensor([1, 1])]; + tensor k_131_pad_type_0 = const()[name = tensor("k_131_pad_type_0"), val = tensor("custom")]; + tensor k_131_pad_0 = const()[name = tensor("k_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(862614208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(864580352))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_131_cast_fp16 = conv(dilations = var_6548, groups = var_4943, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_6546, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_131_cast_fp16")]; + tensor var_6552 = const()[name = tensor("op_6552"), val = tensor([1, 1])]; + tensor var_6554 = const()[name = tensor("op_6554"), val = tensor([1, 1])]; + tensor v_131_pad_type_0 = const()[name = tensor("v_131_pad_type_0"), val = tensor("custom")]; + tensor v_131_pad_0 = const()[name = tensor("v_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(864580544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(866546688))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_131_cast_fp16 = conv(dilations = var_6554, groups = var_4943, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_6552, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_131_cast_fp16")]; + tensor var_6558 = const()[name = tensor("op_6558"), val = tensor([1, 20, 64, -1])]; + tensor var_6559_cast_fp16 = reshape(shape = var_6558, x = q_131_cast_fp16)[name = tensor("op_6559_cast_fp16")]; + tensor var_6560 = const()[name = tensor("op_6560"), val = tensor([1, 20, 64, -1])]; + tensor var_6561_cast_fp16 = reshape(shape = var_6560, x = k_131_cast_fp16)[name = tensor("op_6561_cast_fp16")]; + tensor var_6562 = const()[name = tensor("op_6562"), val = tensor([1, 20, 64, -1])]; + tensor var_6563_cast_fp16 = reshape(shape = var_6562, x = v_131_cast_fp16)[name = tensor("op_6563_cast_fp16")]; + tensor attn_weights_261_transpose_x_0 = const()[name = tensor("attn_weights_261_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_261_transpose_y_0 = const()[name = tensor("attn_weights_261_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_261_cast_fp16 = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_6559_cast_fp16, y = var_6561_cast_fp16)[name = tensor("attn_weights_261_cast_fp16")]; + tensor attn_weights_263_cast_fp16 = mul(x = attn_weights_261_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_263_cast_fp16")]; + tensor var_6567_cast_fp16 = softmax(axis = var_4927, x = attn_weights_263_cast_fp16)[name = tensor("op_6567_cast_fp16")]; + tensor attn_131_transpose_x_0 = const()[name = tensor("attn_131_transpose_x_0"), val = tensor(false)]; + tensor attn_131_transpose_y_0 = const()[name = tensor("attn_131_transpose_y_0"), val = tensor(true)]; + tensor attn_131_cast_fp16 = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_6563_cast_fp16, y = var_6567_cast_fp16)[name = tensor("attn_131_cast_fp16")]; + tensor var_6571 = const()[name = tensor("op_6571"), val = tensor([1, 1280, 1, -1])]; + tensor input_393_cast_fp16 = reshape(shape = var_6571, x = attn_131_cast_fp16)[name = tensor("input_393_cast_fp16")]; + tensor var_6576 = const()[name = tensor("op_6576"), val = tensor([1, 1])]; + tensor var_6578 = const()[name = tensor("op_6578"), val = tensor([1, 1])]; + tensor var_6580_pad_type_0 = const()[name = tensor("op_6580_pad_type_0"), val = tensor("custom")]; + tensor var_6580_pad_0 = const()[name = tensor("op_6580_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(866546880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867775744))), name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867775936)))]; + tensor var_6580_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_6578, groups = var_4943, pad = var_6580_pad_0, pad_type = var_6580_pad_type_0, strides = var_6576, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = tensor("op_6580_cast_fp16")]; + tensor inputs_197_cast_fp16 = add(x = var_6580_cast_fp16, y = inputs_195_cast_fp16)[name = tensor("inputs_197_cast_fp16")]; + tensor input_395_axes_0 = const()[name = tensor("input_395_axes_0"), val = tensor([1])]; + tensor input_395_gamma_0_to_fp16 = const()[name = tensor("input_395_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867778560)))]; + tensor input_395_beta_0_to_fp16 = const()[name = tensor("input_395_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867781184)))]; + tensor var_6590_to_fp16 = const()[name = tensor("op_6590_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_395_cast_fp16 = layer_norm(axes = input_395_axes_0, beta = input_395_beta_0_to_fp16, epsilon = var_6590_to_fp16, gamma = input_395_gamma_0_to_fp16, x = inputs_197_cast_fp16)[name = tensor("input_395_cast_fp16")]; + tensor var_6606 = const()[name = tensor("op_6606"), val = tensor([1, 1])]; + tensor var_6608 = const()[name = tensor("op_6608"), val = tensor([1, 1])]; + tensor var_6610_pad_type_0 = const()[name = tensor("op_6610_pad_type_0"), val = tensor("custom")]; + tensor var_6610_pad_0 = const()[name = tensor("op_6610_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(867783808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877614272))), name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877614464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877622208))), name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6610_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6608, groups = var_4943, pad = var_6610_pad_0, pad_type = var_6610_pad_type_0, strides = var_6606, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_395_cast_fp16)[name = tensor("op_6610_cast_fp16")]; + tensor var_6611_split_sizes_0 = const()[name = tensor("op_6611_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6611_axis_0 = const()[name = tensor("op_6611_axis_0"), val = tensor(1)]; + tensor var_6611_cast_fp16_0, tensor var_6611_cast_fp16_1 = split(axis = var_6611_axis_0, split_sizes = var_6611_split_sizes_0, x = var_6610_cast_fp16)[name = tensor("op_6611_cast_fp16")]; + tensor var_6613_mode_0 = const()[name = tensor("op_6613_mode_0"), val = tensor("EXACT")]; + tensor var_6613_cast_fp16 = gelu(mode = var_6613_mode_0, x = var_6611_cast_fp16_1)[name = tensor("op_6613_cast_fp16")]; + tensor input_397_cast_fp16 = mul(x = var_6611_cast_fp16_0, y = var_6613_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor var_6617 = const()[name = tensor("op_6617"), val = tensor([1, 1])]; + tensor var_6619 = const()[name = tensor("op_6619"), val = tensor([1, 1])]; + tensor var_6621_pad_type_0 = const()[name = tensor("op_6621_pad_type_0"), val = tensor("custom")]; + tensor var_6621_pad_0 = const()[name = tensor("op_6621_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877622400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882537664))), name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882537856)))]; + tensor var_6621_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_6619, groups = var_4943, pad = var_6621_pad_0, pad_type = var_6621_pad_type_0, strides = var_6617, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_397_cast_fp16)[name = tensor("op_6621_cast_fp16")]; + tensor inputs_199_cast_fp16 = add(x = var_6621_cast_fp16, y = inputs_197_cast_fp16)[name = tensor("inputs_199_cast_fp16")]; + tensor hidden_states_263_axes_0 = const()[name = tensor("hidden_states_263_axes_0"), val = tensor([1])]; + tensor hidden_states_263_gamma_0_to_fp16 = const()[name = tensor("hidden_states_263_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882540480)))]; + tensor hidden_states_263_beta_0_to_fp16 = const()[name = tensor("hidden_states_263_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882543104)))]; + tensor var_6637_to_fp16 = const()[name = tensor("op_6637_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_263_cast_fp16 = layer_norm(axes = hidden_states_263_axes_0, beta = hidden_states_263_beta_0_to_fp16, epsilon = var_6637_to_fp16, gamma = hidden_states_263_gamma_0_to_fp16, x = inputs_199_cast_fp16)[name = tensor("hidden_states_263_cast_fp16")]; + tensor var_6652 = const()[name = tensor("op_6652"), val = tensor([1, 1])]; + tensor var_6654 = const()[name = tensor("op_6654"), val = tensor([1, 1])]; + tensor q_133_pad_type_0 = const()[name = tensor("q_133_pad_type_0"), val = tensor("custom")]; + tensor q_133_pad_0 = const()[name = tensor("q_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(882545728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(883774592))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_133_cast_fp16 = conv(dilations = var_6654, groups = var_4943, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_6652, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_263_cast_fp16)[name = tensor("q_133_cast_fp16")]; + tensor var_6658 = const()[name = tensor("op_6658"), val = tensor([1, 1])]; + tensor var_6660 = const()[name = tensor("op_6660"), val = tensor([1, 1])]; + tensor k_133_pad_type_0 = const()[name = tensor("k_133_pad_type_0"), val = tensor("custom")]; + tensor k_133_pad_0 = const()[name = tensor("k_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(883774784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(885003648))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_133_cast_fp16 = conv(dilations = var_6660, groups = var_4943, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_6658, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_263_cast_fp16)[name = tensor("k_133_cast_fp16")]; + tensor var_6664 = const()[name = tensor("op_6664"), val = tensor([1, 1])]; + tensor var_6666 = const()[name = tensor("op_6666"), val = tensor([1, 1])]; + tensor v_133_pad_type_0 = const()[name = tensor("v_133_pad_type_0"), val = tensor("custom")]; + tensor v_133_pad_0 = const()[name = tensor("v_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(885003840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(886232704))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_133_cast_fp16 = conv(dilations = var_6666, groups = var_4943, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_6664, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_263_cast_fp16)[name = tensor("v_133_cast_fp16")]; + tensor var_6670 = const()[name = tensor("op_6670"), val = tensor([1, 20, 64, -1])]; + tensor var_6671_cast_fp16 = reshape(shape = var_6670, x = q_133_cast_fp16)[name = tensor("op_6671_cast_fp16")]; + tensor var_6672 = const()[name = tensor("op_6672"), val = tensor([1, 20, 64, -1])]; + tensor var_6673_cast_fp16 = reshape(shape = var_6672, x = k_133_cast_fp16)[name = tensor("op_6673_cast_fp16")]; + tensor var_6674 = const()[name = tensor("op_6674"), val = tensor([1, 20, 64, -1])]; + tensor var_6675_cast_fp16 = reshape(shape = var_6674, x = v_133_cast_fp16)[name = tensor("op_6675_cast_fp16")]; + tensor attn_weights_265_transpose_x_0 = const()[name = tensor("attn_weights_265_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_265_transpose_y_0 = const()[name = tensor("attn_weights_265_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6671_cast_fp16, y = var_6673_cast_fp16)[name = tensor("attn_weights_265_cast_fp16")]; + tensor attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_267_cast_fp16")]; + tensor var_6679_cast_fp16 = softmax(axis = var_4927, x = attn_weights_267_cast_fp16)[name = tensor("op_6679_cast_fp16")]; + tensor attn_133_transpose_x_0 = const()[name = tensor("attn_133_transpose_x_0"), val = tensor(false)]; + tensor attn_133_transpose_y_0 = const()[name = tensor("attn_133_transpose_y_0"), val = tensor(true)]; + tensor attn_133_cast_fp16 = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_6675_cast_fp16, y = var_6679_cast_fp16)[name = tensor("attn_133_cast_fp16")]; + tensor var_6683 = const()[name = tensor("op_6683"), val = tensor([1, 1280, 1, -1])]; + tensor input_399_cast_fp16 = reshape(shape = var_6683, x = attn_133_cast_fp16)[name = tensor("input_399_cast_fp16")]; + tensor var_6688 = const()[name = tensor("op_6688"), val = tensor([1, 1])]; + tensor var_6690 = const()[name = tensor("op_6690"), val = tensor([1, 1])]; + tensor var_6692_pad_type_0 = const()[name = tensor("op_6692_pad_type_0"), val = tensor("custom")]; + tensor var_6692_pad_0 = const()[name = tensor("op_6692_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(886232896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887461760))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887461952)))]; + tensor var_6692_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_6690, groups = var_4943, pad = var_6692_pad_0, pad_type = var_6692_pad_type_0, strides = var_6688, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_399_cast_fp16)[name = tensor("op_6692_cast_fp16")]; + tensor inputs_201_cast_fp16 = add(x = var_6692_cast_fp16, y = inputs_199_cast_fp16)[name = tensor("inputs_201_cast_fp16")]; + tensor hidden_states_265_axes_0 = const()[name = tensor("hidden_states_265_axes_0"), val = tensor([1])]; + tensor hidden_states_265_gamma_0_to_fp16 = const()[name = tensor("hidden_states_265_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887464576)))]; + tensor hidden_states_265_beta_0_to_fp16 = const()[name = tensor("hidden_states_265_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887467200)))]; + tensor var_6702_to_fp16 = const()[name = tensor("op_6702_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_265_cast_fp16 = layer_norm(axes = hidden_states_265_axes_0, beta = hidden_states_265_beta_0_to_fp16, epsilon = var_6702_to_fp16, gamma = hidden_states_265_gamma_0_to_fp16, x = inputs_201_cast_fp16)[name = tensor("hidden_states_265_cast_fp16")]; + tensor var_6717 = const()[name = tensor("op_6717"), val = tensor([1, 1])]; + tensor var_6719 = const()[name = tensor("op_6719"), val = tensor([1, 1])]; + tensor q_135_pad_type_0 = const()[name = tensor("q_135_pad_type_0"), val = tensor("custom")]; + tensor q_135_pad_0 = const()[name = tensor("q_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887469824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(888698688))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_135_cast_fp16 = conv(dilations = var_6719, groups = var_4943, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_6717, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_265_cast_fp16)[name = tensor("q_135_cast_fp16")]; + tensor var_6723 = const()[name = tensor("op_6723"), val = tensor([1, 1])]; + tensor var_6725 = const()[name = tensor("op_6725"), val = tensor([1, 1])]; + tensor k_135_pad_type_0 = const()[name = tensor("k_135_pad_type_0"), val = tensor("custom")]; + tensor k_135_pad_0 = const()[name = tensor("k_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(888698880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(890665024))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_135_cast_fp16 = conv(dilations = var_6725, groups = var_4943, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_6723, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_135_cast_fp16")]; + tensor var_6729 = const()[name = tensor("op_6729"), val = tensor([1, 1])]; + tensor var_6731 = const()[name = tensor("op_6731"), val = tensor([1, 1])]; + tensor v_135_pad_type_0 = const()[name = tensor("v_135_pad_type_0"), val = tensor("custom")]; + tensor v_135_pad_0 = const()[name = tensor("v_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(890665216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(892631360))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_135_cast_fp16 = conv(dilations = var_6731, groups = var_4943, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_6729, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_135_cast_fp16")]; + tensor var_6735 = const()[name = tensor("op_6735"), val = tensor([1, 20, 64, -1])]; + tensor var_6736_cast_fp16 = reshape(shape = var_6735, x = q_135_cast_fp16)[name = tensor("op_6736_cast_fp16")]; + tensor var_6737 = const()[name = tensor("op_6737"), val = tensor([1, 20, 64, -1])]; + tensor var_6738_cast_fp16 = reshape(shape = var_6737, x = k_135_cast_fp16)[name = tensor("op_6738_cast_fp16")]; + tensor var_6739 = const()[name = tensor("op_6739"), val = tensor([1, 20, 64, -1])]; + tensor var_6740_cast_fp16 = reshape(shape = var_6739, x = v_135_cast_fp16)[name = tensor("op_6740_cast_fp16")]; + tensor attn_weights_269_transpose_x_0 = const()[name = tensor("attn_weights_269_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_269_transpose_y_0 = const()[name = tensor("attn_weights_269_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_269_cast_fp16 = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_6736_cast_fp16, y = var_6738_cast_fp16)[name = tensor("attn_weights_269_cast_fp16")]; + tensor attn_weights_271_cast_fp16 = mul(x = attn_weights_269_cast_fp16, y = var_4934_to_fp16)[name = tensor("attn_weights_271_cast_fp16")]; + tensor var_6744_cast_fp16 = softmax(axis = var_4927, x = attn_weights_271_cast_fp16)[name = tensor("op_6744_cast_fp16")]; + tensor attn_135_transpose_x_0 = const()[name = tensor("attn_135_transpose_x_0"), val = tensor(false)]; + tensor attn_135_transpose_y_0 = const()[name = tensor("attn_135_transpose_y_0"), val = tensor(true)]; + tensor attn_135_cast_fp16 = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_6740_cast_fp16, y = var_6744_cast_fp16)[name = tensor("attn_135_cast_fp16")]; + tensor var_6748 = const()[name = tensor("op_6748"), val = tensor([1, 1280, 1, -1])]; + tensor input_401_cast_fp16 = reshape(shape = var_6748, x = attn_135_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor var_6753 = const()[name = tensor("op_6753"), val = tensor([1, 1])]; + tensor var_6755 = const()[name = tensor("op_6755"), val = tensor([1, 1])]; + tensor var_6757_pad_type_0 = const()[name = tensor("op_6757_pad_type_0"), val = tensor("custom")]; + tensor var_6757_pad_0 = const()[name = tensor("op_6757_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(892631552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893860416))), name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893860608)))]; + tensor var_6757_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_6755, groups = var_4943, pad = var_6757_pad_0, pad_type = var_6757_pad_type_0, strides = var_6753, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = tensor("op_6757_cast_fp16")]; + tensor inputs_203_cast_fp16 = add(x = var_6757_cast_fp16, y = inputs_201_cast_fp16)[name = tensor("inputs_203_cast_fp16")]; + tensor input_403_axes_0 = const()[name = tensor("input_403_axes_0"), val = tensor([1])]; + tensor input_403_gamma_0_to_fp16 = const()[name = tensor("input_403_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893863232)))]; + tensor input_403_beta_0_to_fp16 = const()[name = tensor("input_403_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893865856)))]; + tensor var_6767_to_fp16 = const()[name = tensor("op_6767_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_403_cast_fp16 = layer_norm(axes = input_403_axes_0, beta = input_403_beta_0_to_fp16, epsilon = var_6767_to_fp16, gamma = input_403_gamma_0_to_fp16, x = inputs_203_cast_fp16)[name = tensor("input_403_cast_fp16")]; + tensor var_6783 = const()[name = tensor("op_6783"), val = tensor([1, 1])]; + tensor var_6785 = const()[name = tensor("op_6785"), val = tensor([1, 1])]; + tensor var_6787_pad_type_0 = const()[name = tensor("op_6787_pad_type_0"), val = tensor("custom")]; + tensor var_6787_pad_0 = const()[name = tensor("op_6787_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893868480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903698944))), name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903699136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903706880))), name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_6787_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_6785, groups = var_4943, pad = var_6787_pad_0, pad_type = var_6787_pad_type_0, strides = var_6783, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_403_cast_fp16)[name = tensor("op_6787_cast_fp16")]; + tensor var_6788_split_sizes_0 = const()[name = tensor("op_6788_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6788_axis_0 = const()[name = tensor("op_6788_axis_0"), val = tensor(1)]; + tensor var_6788_cast_fp16_0, tensor var_6788_cast_fp16_1 = split(axis = var_6788_axis_0, split_sizes = var_6788_split_sizes_0, x = var_6787_cast_fp16)[name = tensor("op_6788_cast_fp16")]; + tensor var_6790_mode_0 = const()[name = tensor("op_6790_mode_0"), val = tensor("EXACT")]; + tensor var_6790_cast_fp16 = gelu(mode = var_6790_mode_0, x = var_6788_cast_fp16_1)[name = tensor("op_6790_cast_fp16")]; + tensor input_405_cast_fp16 = mul(x = var_6788_cast_fp16_0, y = var_6790_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor var_6794 = const()[name = tensor("op_6794"), val = tensor([1, 1])]; + tensor var_6796 = const()[name = tensor("op_6796"), val = tensor([1, 1])]; + tensor var_6798_pad_type_0 = const()[name = tensor("op_6798_pad_type_0"), val = tensor("custom")]; + tensor var_6798_pad_0 = const()[name = tensor("op_6798_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(903707072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(908622336))), name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(908622528)))]; + tensor var_6798_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_6796, groups = var_4943, pad = var_6798_pad_0, pad_type = var_6798_pad_type_0, strides = var_6794, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = tensor("op_6798_cast_fp16")]; + tensor hidden_states_269_cast_fp16 = add(x = var_6798_cast_fp16, y = inputs_203_cast_fp16)[name = tensor("hidden_states_269_cast_fp16")]; + tensor var_6800 = const()[name = tensor("op_6800"), val = tensor([1, 1280, 32, 32])]; + tensor input_407_cast_fp16 = reshape(shape = var_6800, x = hidden_states_269_cast_fp16)[name = tensor("input_407_cast_fp16")]; + tensor var_6804 = const()[name = tensor("op_6804"), val = tensor([1, 1])]; + tensor var_6806 = const()[name = tensor("op_6806"), val = tensor([1, 1])]; + tensor hidden_states_271_pad_type_0 = const()[name = tensor("hidden_states_271_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_271_pad_0 = const()[name = tensor("hidden_states_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(908625152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(909854016))), name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(909854208)))]; + tensor hidden_states_271_cast_fp16 = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_6806, groups = var_4943, pad = hidden_states_271_pad_0, pad_type = hidden_states_271_pad_type_0, strides = var_6804, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = tensor("hidden_states_271_cast_fp16")]; + tensor input_409_cast_fp16 = add(x = hidden_states_271_cast_fp16, y = hidden_states_205_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_409_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(909856832)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(909859456)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor input_413_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("input_413_cast_fp16")]; + tensor var_6821 = const()[name = tensor("op_6821"), val = tensor([1, 1])]; + tensor var_6823 = const()[name = tensor("op_6823"), val = tensor([1, 1])]; + tensor hidden_states_273_pad_type_0 = const()[name = tensor("hidden_states_273_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_273_pad_0 = const()[name = tensor("hidden_states_273_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(909862080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(920921344))), name = tensor("mid_block_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(920921536)))]; + tensor hidden_states_273_cast_fp16 = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_6823, groups = var_4943, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = var_6821, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = tensor("hidden_states_273_cast_fp16")]; + tensor var_6829 = const()[name = tensor("op_6829"), val = tensor([1, 1])]; + tensor var_6831 = const()[name = tensor("op_6831"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(920924160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(922153024))), name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(922153216)))]; + tensor temb_15_cast_fp16 = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_6831, groups = var_4943, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_6829, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_15_cast_fp16")]; + tensor input_417_cast_fp16 = add(x = hidden_states_273_cast_fp16, y = temb_15_cast_fp16)[name = tensor("input_417_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_417_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(922155840)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(922158464)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_421_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_421_cast_fp16")]; + tensor var_6841 = const()[name = tensor("op_6841"), val = tensor([1, 1])]; + tensor var_6843 = const()[name = tensor("op_6843"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(922161088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933220352))), name = tensor("mid_block_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933220544)))]; + tensor hidden_states_275_cast_fp16 = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_6843, groups = var_4943, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_6841, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_421_cast_fp16)[name = tensor("hidden_states_275_cast_fp16")]; + tensor hidden_states_277_cast_fp16 = add(x = input_409_cast_fp16, y = hidden_states_275_cast_fp16)[name = tensor("hidden_states_277_cast_fp16")]; + tensor var_6849 = const()[name = tensor("op_6849"), val = tensor(3)]; + tensor var_6865 = const()[name = tensor("op_6865"), val = tensor(1)]; + tensor input_423_interleave_0 = const()[name = tensor("input_423_interleave_0"), val = tensor(false)]; + tensor input_423_cast_fp16 = concat(axis = var_6865, interleave = input_423_interleave_0, values = (hidden_states_277_cast_fp16, input_311_cast_fp16))[name = tensor("input_423_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 80, 32, 32])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_423_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 2560, 32, 32])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_mean_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933223168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933225152))), name = tensor("add_43_mean_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_variance_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933225344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933227328))), name = tensor("add_43_variance_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933227520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933229504))), name = tensor("add_43_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933229696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933231680))), name = tensor("add_43_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16_palettized, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16_palettized, mean = add_43_mean_0_to_fp16_palettized, variance = add_43_variance_0_to_fp16_palettized, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor input_427_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_427_cast_fp16")]; + tensor var_6894 = const()[name = tensor("op_6894"), val = tensor([1, 1])]; + tensor var_6896 = const()[name = tensor("op_6896"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(933231872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(955350336))), name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(955350528)))]; + tensor hidden_states_279_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_6896, groups = var_6865, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_6894, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_427_cast_fp16)[name = tensor("hidden_states_279_cast_fp16")]; + tensor var_6902 = const()[name = tensor("op_6902"), val = tensor([1, 1])]; + tensor var_6904 = const()[name = tensor("op_6904"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(955353152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956582016))), name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956582208)))]; + tensor temb_17_cast_fp16 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_6904, groups = var_6865, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_6902, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_17_cast_fp16")]; + tensor input_431_cast_fp16 = add(x = hidden_states_279_cast_fp16, y = temb_17_cast_fp16)[name = tensor("input_431_cast_fp16")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_431_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956584832)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956587456)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_435_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_435_cast_fp16")]; + tensor var_6914 = const()[name = tensor("op_6914"), val = tensor([1, 1])]; + tensor var_6916 = const()[name = tensor("op_6916"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956590080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(967649344))), name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(967649536)))]; + tensor hidden_states_281_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_6916, groups = var_6865, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_6914, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_435_cast_fp16)[name = tensor("hidden_states_281_cast_fp16")]; + tensor var_6921 = const()[name = tensor("op_6921"), val = tensor([1, 1])]; + tensor var_6923 = const()[name = tensor("op_6923"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(967652160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(970109824))), name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(970110016)))]; + tensor x_5_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_6923, groups = var_6865, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_6921, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_423_cast_fp16)[name = tensor("x_5_cast_fp16")]; + tensor hidden_states_283_cast_fp16 = add(x = x_5_cast_fp16, y = hidden_states_281_cast_fp16)[name = tensor("hidden_states_283_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = hidden_states_283_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(970112640)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(970115264)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor var_6961 = const()[name = tensor("op_6961"), val = tensor([1, 1])]; + tensor var_6963 = const()[name = tensor("op_6963"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(970117888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971346752))), name = tensor("up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971346944)))]; + tensor hidden_states_285_cast_fp16 = conv(bias = up_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_6963, groups = var_6865, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_6961, weight = up_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_47_cast_fp16)[name = tensor("hidden_states_285_cast_fp16")]; + tensor var_6968 = const()[name = tensor("op_6968"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_205_cast_fp16 = reshape(shape = var_6968, x = hidden_states_285_cast_fp16)[name = tensor("inputs_205_cast_fp16")]; + tensor hidden_states_287_axes_0 = const()[name = tensor("hidden_states_287_axes_0"), val = tensor([1])]; + tensor hidden_states_287_gamma_0_to_fp16 = const()[name = tensor("hidden_states_287_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971349568)))]; + tensor hidden_states_287_beta_0_to_fp16 = const()[name = tensor("hidden_states_287_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971352192)))]; + tensor var_6984_to_fp16 = const()[name = tensor("op_6984_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_287_cast_fp16 = layer_norm(axes = hidden_states_287_axes_0, beta = hidden_states_287_beta_0_to_fp16, epsilon = var_6984_to_fp16, gamma = hidden_states_287_gamma_0_to_fp16, x = inputs_205_cast_fp16)[name = tensor("hidden_states_287_cast_fp16")]; + tensor var_6999 = const()[name = tensor("op_6999"), val = tensor([1, 1])]; + tensor var_7001 = const()[name = tensor("op_7001"), val = tensor([1, 1])]; + tensor q_137_pad_type_0 = const()[name = tensor("q_137_pad_type_0"), val = tensor("custom")]; + tensor q_137_pad_0 = const()[name = tensor("q_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971354816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(972583680))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_137_cast_fp16 = conv(dilations = var_7001, groups = var_6865, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_6999, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("q_137_cast_fp16")]; + tensor var_7005 = const()[name = tensor("op_7005"), val = tensor([1, 1])]; + tensor var_7007 = const()[name = tensor("op_7007"), val = tensor([1, 1])]; + tensor k_137_pad_type_0 = const()[name = tensor("k_137_pad_type_0"), val = tensor("custom")]; + tensor k_137_pad_0 = const()[name = tensor("k_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(972583872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973812736))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_137_cast_fp16 = conv(dilations = var_7007, groups = var_6865, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_7005, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("k_137_cast_fp16")]; + tensor var_7011 = const()[name = tensor("op_7011"), val = tensor([1, 1])]; + tensor var_7013 = const()[name = tensor("op_7013"), val = tensor([1, 1])]; + tensor v_137_pad_type_0 = const()[name = tensor("v_137_pad_type_0"), val = tensor("custom")]; + tensor v_137_pad_0 = const()[name = tensor("v_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(973812928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975041792))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_137_cast_fp16 = conv(dilations = var_7013, groups = var_6865, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_7011, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast_fp16)[name = tensor("v_137_cast_fp16")]; + tensor var_7017 = const()[name = tensor("op_7017"), val = tensor([1, 20, 64, -1])]; + tensor var_7018_cast_fp16 = reshape(shape = var_7017, x = q_137_cast_fp16)[name = tensor("op_7018_cast_fp16")]; + tensor var_7019 = const()[name = tensor("op_7019"), val = tensor([1, 20, 64, -1])]; + tensor var_7020_cast_fp16 = reshape(shape = var_7019, x = k_137_cast_fp16)[name = tensor("op_7020_cast_fp16")]; + tensor var_7021 = const()[name = tensor("op_7021"), val = tensor([1, 20, 64, -1])]; + tensor var_7022_cast_fp16 = reshape(shape = var_7021, x = v_137_cast_fp16)[name = tensor("op_7022_cast_fp16")]; + tensor attn_weights_273_transpose_x_0 = const()[name = tensor("attn_weights_273_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_273_transpose_y_0 = const()[name = tensor("attn_weights_273_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_7018_cast_fp16, y = var_7020_cast_fp16)[name = tensor("attn_weights_273_cast_fp16")]; + tensor var_6856_to_fp16 = const()[name = tensor("op_6856_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_275_cast_fp16")]; + tensor var_7026_cast_fp16 = softmax(axis = var_6849, x = attn_weights_275_cast_fp16)[name = tensor("op_7026_cast_fp16")]; + tensor attn_137_transpose_x_0 = const()[name = tensor("attn_137_transpose_x_0"), val = tensor(false)]; + tensor attn_137_transpose_y_0 = const()[name = tensor("attn_137_transpose_y_0"), val = tensor(true)]; + tensor attn_137_cast_fp16 = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_7022_cast_fp16, y = var_7026_cast_fp16)[name = tensor("attn_137_cast_fp16")]; + tensor var_7030 = const()[name = tensor("op_7030"), val = tensor([1, 1280, 1, -1])]; + tensor input_439_cast_fp16 = reshape(shape = var_7030, x = attn_137_cast_fp16)[name = tensor("input_439_cast_fp16")]; + tensor var_7035 = const()[name = tensor("op_7035"), val = tensor([1, 1])]; + tensor var_7037 = const()[name = tensor("op_7037"), val = tensor([1, 1])]; + tensor var_7039_pad_type_0 = const()[name = tensor("op_7039_pad_type_0"), val = tensor("custom")]; + tensor var_7039_pad_0 = const()[name = tensor("op_7039_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975041984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976270848))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976271040)))]; + tensor var_7039_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7037, groups = var_6865, pad = var_7039_pad_0, pad_type = var_7039_pad_type_0, strides = var_7035, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_439_cast_fp16)[name = tensor("op_7039_cast_fp16")]; + tensor inputs_207_cast_fp16 = add(x = var_7039_cast_fp16, y = inputs_205_cast_fp16)[name = tensor("inputs_207_cast_fp16")]; + tensor hidden_states_289_axes_0 = const()[name = tensor("hidden_states_289_axes_0"), val = tensor([1])]; + tensor hidden_states_289_gamma_0_to_fp16 = const()[name = tensor("hidden_states_289_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976273664)))]; + tensor hidden_states_289_beta_0_to_fp16 = const()[name = tensor("hidden_states_289_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976276288)))]; + tensor var_7049_to_fp16 = const()[name = tensor("op_7049_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_289_cast_fp16 = layer_norm(axes = hidden_states_289_axes_0, beta = hidden_states_289_beta_0_to_fp16, epsilon = var_7049_to_fp16, gamma = hidden_states_289_gamma_0_to_fp16, x = inputs_207_cast_fp16)[name = tensor("hidden_states_289_cast_fp16")]; + tensor var_7064 = const()[name = tensor("op_7064"), val = tensor([1, 1])]; + tensor var_7066 = const()[name = tensor("op_7066"), val = tensor([1, 1])]; + tensor q_139_pad_type_0 = const()[name = tensor("q_139_pad_type_0"), val = tensor("custom")]; + tensor q_139_pad_0 = const()[name = tensor("q_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(976278912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(977507776))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_139_cast_fp16 = conv(dilations = var_7066, groups = var_6865, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_7064, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast_fp16)[name = tensor("q_139_cast_fp16")]; + tensor var_7070 = const()[name = tensor("op_7070"), val = tensor([1, 1])]; + tensor var_7072 = const()[name = tensor("op_7072"), val = tensor([1, 1])]; + tensor k_139_pad_type_0 = const()[name = tensor("k_139_pad_type_0"), val = tensor("custom")]; + tensor k_139_pad_0 = const()[name = tensor("k_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(977507968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(979474112))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_139_cast_fp16 = conv(dilations = var_7072, groups = var_6865, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_7070, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_139_cast_fp16")]; + tensor var_7076 = const()[name = tensor("op_7076"), val = tensor([1, 1])]; + tensor var_7078 = const()[name = tensor("op_7078"), val = tensor([1, 1])]; + tensor v_139_pad_type_0 = const()[name = tensor("v_139_pad_type_0"), val = tensor("custom")]; + tensor v_139_pad_0 = const()[name = tensor("v_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(979474304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(981440448))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_139_cast_fp16 = conv(dilations = var_7078, groups = var_6865, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_7076, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_139_cast_fp16")]; + tensor var_7082 = const()[name = tensor("op_7082"), val = tensor([1, 20, 64, -1])]; + tensor var_7083_cast_fp16 = reshape(shape = var_7082, x = q_139_cast_fp16)[name = tensor("op_7083_cast_fp16")]; + tensor var_7084 = const()[name = tensor("op_7084"), val = tensor([1, 20, 64, -1])]; + tensor var_7085_cast_fp16 = reshape(shape = var_7084, x = k_139_cast_fp16)[name = tensor("op_7085_cast_fp16")]; + tensor var_7086 = const()[name = tensor("op_7086"), val = tensor([1, 20, 64, -1])]; + tensor var_7087_cast_fp16 = reshape(shape = var_7086, x = v_139_cast_fp16)[name = tensor("op_7087_cast_fp16")]; + tensor attn_weights_277_transpose_x_0 = const()[name = tensor("attn_weights_277_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_277_transpose_y_0 = const()[name = tensor("attn_weights_277_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_277_cast_fp16 = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_7083_cast_fp16, y = var_7085_cast_fp16)[name = tensor("attn_weights_277_cast_fp16")]; + tensor attn_weights_279_cast_fp16 = mul(x = attn_weights_277_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_279_cast_fp16")]; + tensor var_7091_cast_fp16 = softmax(axis = var_6849, x = attn_weights_279_cast_fp16)[name = tensor("op_7091_cast_fp16")]; + tensor attn_139_transpose_x_0 = const()[name = tensor("attn_139_transpose_x_0"), val = tensor(false)]; + tensor attn_139_transpose_y_0 = const()[name = tensor("attn_139_transpose_y_0"), val = tensor(true)]; + tensor attn_139_cast_fp16 = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_7087_cast_fp16, y = var_7091_cast_fp16)[name = tensor("attn_139_cast_fp16")]; + tensor var_7095 = const()[name = tensor("op_7095"), val = tensor([1, 1280, 1, -1])]; + tensor input_441_cast_fp16 = reshape(shape = var_7095, x = attn_139_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor var_7100 = const()[name = tensor("op_7100"), val = tensor([1, 1])]; + tensor var_7102 = const()[name = tensor("op_7102"), val = tensor([1, 1])]; + tensor var_7104_pad_type_0 = const()[name = tensor("op_7104_pad_type_0"), val = tensor("custom")]; + tensor var_7104_pad_0 = const()[name = tensor("op_7104_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(981440640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982669504))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982669696)))]; + tensor var_7104_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7102, groups = var_6865, pad = var_7104_pad_0, pad_type = var_7104_pad_type_0, strides = var_7100, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_441_cast_fp16)[name = tensor("op_7104_cast_fp16")]; + tensor inputs_209_cast_fp16 = add(x = var_7104_cast_fp16, y = inputs_207_cast_fp16)[name = tensor("inputs_209_cast_fp16")]; + tensor input_443_axes_0 = const()[name = tensor("input_443_axes_0"), val = tensor([1])]; + tensor input_443_gamma_0_to_fp16 = const()[name = tensor("input_443_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982672320)))]; + tensor input_443_beta_0_to_fp16 = const()[name = tensor("input_443_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982674944)))]; + tensor var_7114_to_fp16 = const()[name = tensor("op_7114_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = input_443_beta_0_to_fp16, epsilon = var_7114_to_fp16, gamma = input_443_gamma_0_to_fp16, x = inputs_209_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor var_7130 = const()[name = tensor("op_7130"), val = tensor([1, 1])]; + tensor var_7132 = const()[name = tensor("op_7132"), val = tensor([1, 1])]; + tensor var_7134_pad_type_0 = const()[name = tensor("op_7134_pad_type_0"), val = tensor("custom")]; + tensor var_7134_pad_0 = const()[name = tensor("op_7134_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(982677568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992508032))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992508224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992515968))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7134_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7132, groups = var_6865, pad = var_7134_pad_0, pad_type = var_7134_pad_type_0, strides = var_7130, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_443_cast_fp16)[name = tensor("op_7134_cast_fp16")]; + tensor var_7135_split_sizes_0 = const()[name = tensor("op_7135_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7135_axis_0 = const()[name = tensor("op_7135_axis_0"), val = tensor(1)]; + tensor var_7135_cast_fp16_0, tensor var_7135_cast_fp16_1 = split(axis = var_7135_axis_0, split_sizes = var_7135_split_sizes_0, x = var_7134_cast_fp16)[name = tensor("op_7135_cast_fp16")]; + tensor var_7137_mode_0 = const()[name = tensor("op_7137_mode_0"), val = tensor("EXACT")]; + tensor var_7137_cast_fp16 = gelu(mode = var_7137_mode_0, x = var_7135_cast_fp16_1)[name = tensor("op_7137_cast_fp16")]; + tensor input_445_cast_fp16 = mul(x = var_7135_cast_fp16_0, y = var_7137_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor var_7141 = const()[name = tensor("op_7141"), val = tensor([1, 1])]; + tensor var_7143 = const()[name = tensor("op_7143"), val = tensor([1, 1])]; + tensor var_7145_pad_type_0 = const()[name = tensor("op_7145_pad_type_0"), val = tensor("custom")]; + tensor var_7145_pad_0 = const()[name = tensor("op_7145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992516160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997431424))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997431616)))]; + tensor var_7145_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7143, groups = var_6865, pad = var_7145_pad_0, pad_type = var_7145_pad_type_0, strides = var_7141, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_445_cast_fp16)[name = tensor("op_7145_cast_fp16")]; + tensor inputs_211_cast_fp16 = add(x = var_7145_cast_fp16, y = inputs_209_cast_fp16)[name = tensor("inputs_211_cast_fp16")]; + tensor hidden_states_293_axes_0 = const()[name = tensor("hidden_states_293_axes_0"), val = tensor([1])]; + tensor hidden_states_293_gamma_0_to_fp16 = const()[name = tensor("hidden_states_293_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997434240)))]; + tensor hidden_states_293_beta_0_to_fp16 = const()[name = tensor("hidden_states_293_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997436864)))]; + tensor var_7161_to_fp16 = const()[name = tensor("op_7161_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_293_cast_fp16 = layer_norm(axes = hidden_states_293_axes_0, beta = hidden_states_293_beta_0_to_fp16, epsilon = var_7161_to_fp16, gamma = hidden_states_293_gamma_0_to_fp16, x = inputs_211_cast_fp16)[name = tensor("hidden_states_293_cast_fp16")]; + tensor var_7176 = const()[name = tensor("op_7176"), val = tensor([1, 1])]; + tensor var_7178 = const()[name = tensor("op_7178"), val = tensor([1, 1])]; + tensor q_141_pad_type_0 = const()[name = tensor("q_141_pad_type_0"), val = tensor("custom")]; + tensor q_141_pad_0 = const()[name = tensor("q_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997439488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(998668352))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_141_cast_fp16 = conv(dilations = var_7178, groups = var_6865, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7176, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_293_cast_fp16)[name = tensor("q_141_cast_fp16")]; + tensor var_7182 = const()[name = tensor("op_7182"), val = tensor([1, 1])]; + tensor var_7184 = const()[name = tensor("op_7184"), val = tensor([1, 1])]; + tensor k_141_pad_type_0 = const()[name = tensor("k_141_pad_type_0"), val = tensor("custom")]; + tensor k_141_pad_0 = const()[name = tensor("k_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(998668544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(999897408))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_141_cast_fp16 = conv(dilations = var_7184, groups = var_6865, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7182, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_293_cast_fp16)[name = tensor("k_141_cast_fp16")]; + tensor var_7188 = const()[name = tensor("op_7188"), val = tensor([1, 1])]; + tensor var_7190 = const()[name = tensor("op_7190"), val = tensor([1, 1])]; + tensor v_141_pad_type_0 = const()[name = tensor("v_141_pad_type_0"), val = tensor("custom")]; + tensor v_141_pad_0 = const()[name = tensor("v_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(999897600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1001126464))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_141_cast_fp16 = conv(dilations = var_7190, groups = var_6865, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7188, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_293_cast_fp16)[name = tensor("v_141_cast_fp16")]; + tensor var_7194 = const()[name = tensor("op_7194"), val = tensor([1, 20, 64, -1])]; + tensor var_7195_cast_fp16 = reshape(shape = var_7194, x = q_141_cast_fp16)[name = tensor("op_7195_cast_fp16")]; + tensor var_7196 = const()[name = tensor("op_7196"), val = tensor([1, 20, 64, -1])]; + tensor var_7197_cast_fp16 = reshape(shape = var_7196, x = k_141_cast_fp16)[name = tensor("op_7197_cast_fp16")]; + tensor var_7198 = const()[name = tensor("op_7198"), val = tensor([1, 20, 64, -1])]; + tensor var_7199_cast_fp16 = reshape(shape = var_7198, x = v_141_cast_fp16)[name = tensor("op_7199_cast_fp16")]; + tensor attn_weights_281_transpose_x_0 = const()[name = tensor("attn_weights_281_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_281_transpose_y_0 = const()[name = tensor("attn_weights_281_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7195_cast_fp16, y = var_7197_cast_fp16)[name = tensor("attn_weights_281_cast_fp16")]; + tensor attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_283_cast_fp16")]; + tensor var_7203_cast_fp16 = softmax(axis = var_6849, x = attn_weights_283_cast_fp16)[name = tensor("op_7203_cast_fp16")]; + tensor attn_141_transpose_x_0 = const()[name = tensor("attn_141_transpose_x_0"), val = tensor(false)]; + tensor attn_141_transpose_y_0 = const()[name = tensor("attn_141_transpose_y_0"), val = tensor(true)]; + tensor attn_141_cast_fp16 = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7199_cast_fp16, y = var_7203_cast_fp16)[name = tensor("attn_141_cast_fp16")]; + tensor var_7207 = const()[name = tensor("op_7207"), val = tensor([1, 1280, 1, -1])]; + tensor input_447_cast_fp16 = reshape(shape = var_7207, x = attn_141_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor var_7212 = const()[name = tensor("op_7212"), val = tensor([1, 1])]; + tensor var_7214 = const()[name = tensor("op_7214"), val = tensor([1, 1])]; + tensor var_7216_pad_type_0 = const()[name = tensor("op_7216_pad_type_0"), val = tensor("custom")]; + tensor var_7216_pad_0 = const()[name = tensor("op_7216_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1001126656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002355520))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002355712)))]; + tensor var_7216_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_7214, groups = var_6865, pad = var_7216_pad_0, pad_type = var_7216_pad_type_0, strides = var_7212, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = tensor("op_7216_cast_fp16")]; + tensor inputs_213_cast_fp16 = add(x = var_7216_cast_fp16, y = inputs_211_cast_fp16)[name = tensor("inputs_213_cast_fp16")]; + tensor hidden_states_295_axes_0 = const()[name = tensor("hidden_states_295_axes_0"), val = tensor([1])]; + tensor hidden_states_295_gamma_0_to_fp16 = const()[name = tensor("hidden_states_295_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002358336)))]; + tensor hidden_states_295_beta_0_to_fp16 = const()[name = tensor("hidden_states_295_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002360960)))]; + tensor var_7226_to_fp16 = const()[name = tensor("op_7226_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_295_cast_fp16 = layer_norm(axes = hidden_states_295_axes_0, beta = hidden_states_295_beta_0_to_fp16, epsilon = var_7226_to_fp16, gamma = hidden_states_295_gamma_0_to_fp16, x = inputs_213_cast_fp16)[name = tensor("hidden_states_295_cast_fp16")]; + tensor var_7241 = const()[name = tensor("op_7241"), val = tensor([1, 1])]; + tensor var_7243 = const()[name = tensor("op_7243"), val = tensor([1, 1])]; + tensor q_143_pad_type_0 = const()[name = tensor("q_143_pad_type_0"), val = tensor("custom")]; + tensor q_143_pad_0 = const()[name = tensor("q_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002363584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1003592448))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_143_cast_fp16 = conv(dilations = var_7243, groups = var_6865, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7241, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_295_cast_fp16)[name = tensor("q_143_cast_fp16")]; + tensor var_7247 = const()[name = tensor("op_7247"), val = tensor([1, 1])]; + tensor var_7249 = const()[name = tensor("op_7249"), val = tensor([1, 1])]; + tensor k_143_pad_type_0 = const()[name = tensor("k_143_pad_type_0"), val = tensor("custom")]; + tensor k_143_pad_0 = const()[name = tensor("k_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1003592640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005558784))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_143_cast_fp16 = conv(dilations = var_7249, groups = var_6865, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7247, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_143_cast_fp16")]; + tensor var_7253 = const()[name = tensor("op_7253"), val = tensor([1, 1])]; + tensor var_7255 = const()[name = tensor("op_7255"), val = tensor([1, 1])]; + tensor v_143_pad_type_0 = const()[name = tensor("v_143_pad_type_0"), val = tensor("custom")]; + tensor v_143_pad_0 = const()[name = tensor("v_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005558976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007525120))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_143_cast_fp16 = conv(dilations = var_7255, groups = var_6865, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7253, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_143_cast_fp16")]; + tensor var_7259 = const()[name = tensor("op_7259"), val = tensor([1, 20, 64, -1])]; + tensor var_7260_cast_fp16 = reshape(shape = var_7259, x = q_143_cast_fp16)[name = tensor("op_7260_cast_fp16")]; + tensor var_7261 = const()[name = tensor("op_7261"), val = tensor([1, 20, 64, -1])]; + tensor var_7262_cast_fp16 = reshape(shape = var_7261, x = k_143_cast_fp16)[name = tensor("op_7262_cast_fp16")]; + tensor var_7263 = const()[name = tensor("op_7263"), val = tensor([1, 20, 64, -1])]; + tensor var_7264_cast_fp16 = reshape(shape = var_7263, x = v_143_cast_fp16)[name = tensor("op_7264_cast_fp16")]; + tensor attn_weights_285_transpose_x_0 = const()[name = tensor("attn_weights_285_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_285_transpose_y_0 = const()[name = tensor("attn_weights_285_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_285_cast_fp16 = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7260_cast_fp16, y = var_7262_cast_fp16)[name = tensor("attn_weights_285_cast_fp16")]; + tensor attn_weights_287_cast_fp16 = mul(x = attn_weights_285_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_287_cast_fp16")]; + tensor var_7268_cast_fp16 = softmax(axis = var_6849, x = attn_weights_287_cast_fp16)[name = tensor("op_7268_cast_fp16")]; + tensor attn_143_transpose_x_0 = const()[name = tensor("attn_143_transpose_x_0"), val = tensor(false)]; + tensor attn_143_transpose_y_0 = const()[name = tensor("attn_143_transpose_y_0"), val = tensor(true)]; + tensor attn_143_cast_fp16 = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7264_cast_fp16, y = var_7268_cast_fp16)[name = tensor("attn_143_cast_fp16")]; + tensor var_7272 = const()[name = tensor("op_7272"), val = tensor([1, 1280, 1, -1])]; + tensor input_449_cast_fp16 = reshape(shape = var_7272, x = attn_143_cast_fp16)[name = tensor("input_449_cast_fp16")]; + tensor var_7277 = const()[name = tensor("op_7277"), val = tensor([1, 1])]; + tensor var_7279 = const()[name = tensor("op_7279"), val = tensor([1, 1])]; + tensor var_7281_pad_type_0 = const()[name = tensor("op_7281_pad_type_0"), val = tensor("custom")]; + tensor var_7281_pad_0 = const()[name = tensor("op_7281_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1007525312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008754176))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008754368)))]; + tensor var_7281_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_7279, groups = var_6865, pad = var_7281_pad_0, pad_type = var_7281_pad_type_0, strides = var_7277, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_449_cast_fp16)[name = tensor("op_7281_cast_fp16")]; + tensor inputs_215_cast_fp16 = add(x = var_7281_cast_fp16, y = inputs_213_cast_fp16)[name = tensor("inputs_215_cast_fp16")]; + tensor input_451_axes_0 = const()[name = tensor("input_451_axes_0"), val = tensor([1])]; + tensor input_451_gamma_0_to_fp16 = const()[name = tensor("input_451_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008756992)))]; + tensor input_451_beta_0_to_fp16 = const()[name = tensor("input_451_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008759616)))]; + tensor var_7291_to_fp16 = const()[name = tensor("op_7291_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_451_cast_fp16 = layer_norm(axes = input_451_axes_0, beta = input_451_beta_0_to_fp16, epsilon = var_7291_to_fp16, gamma = input_451_gamma_0_to_fp16, x = inputs_215_cast_fp16)[name = tensor("input_451_cast_fp16")]; + tensor var_7307 = const()[name = tensor("op_7307"), val = tensor([1, 1])]; + tensor var_7309 = const()[name = tensor("op_7309"), val = tensor([1, 1])]; + tensor var_7311_pad_type_0 = const()[name = tensor("op_7311_pad_type_0"), val = tensor("custom")]; + tensor var_7311_pad_0 = const()[name = tensor("op_7311_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1008762240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018592704))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018592896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018600640))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7311_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7309, groups = var_6865, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7307, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_451_cast_fp16)[name = tensor("op_7311_cast_fp16")]; + tensor var_7312_split_sizes_0 = const()[name = tensor("op_7312_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7312_axis_0 = const()[name = tensor("op_7312_axis_0"), val = tensor(1)]; + tensor var_7312_cast_fp16_0, tensor var_7312_cast_fp16_1 = split(axis = var_7312_axis_0, split_sizes = var_7312_split_sizes_0, x = var_7311_cast_fp16)[name = tensor("op_7312_cast_fp16")]; + tensor var_7314_mode_0 = const()[name = tensor("op_7314_mode_0"), val = tensor("EXACT")]; + tensor var_7314_cast_fp16 = gelu(mode = var_7314_mode_0, x = var_7312_cast_fp16_1)[name = tensor("op_7314_cast_fp16")]; + tensor input_453_cast_fp16 = mul(x = var_7312_cast_fp16_0, y = var_7314_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor var_7318 = const()[name = tensor("op_7318"), val = tensor([1, 1])]; + tensor var_7320 = const()[name = tensor("op_7320"), val = tensor([1, 1])]; + tensor var_7322_pad_type_0 = const()[name = tensor("op_7322_pad_type_0"), val = tensor("custom")]; + tensor var_7322_pad_0 = const()[name = tensor("op_7322_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018600832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023516096))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023516288)))]; + tensor var_7322_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_7320, groups = var_6865, pad = var_7322_pad_0, pad_type = var_7322_pad_type_0, strides = var_7318, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_453_cast_fp16)[name = tensor("op_7322_cast_fp16")]; + tensor inputs_217_cast_fp16 = add(x = var_7322_cast_fp16, y = inputs_215_cast_fp16)[name = tensor("inputs_217_cast_fp16")]; + tensor hidden_states_299_axes_0 = const()[name = tensor("hidden_states_299_axes_0"), val = tensor([1])]; + tensor hidden_states_299_gamma_0_to_fp16 = const()[name = tensor("hidden_states_299_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023518912)))]; + tensor hidden_states_299_beta_0_to_fp16 = const()[name = tensor("hidden_states_299_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023521536)))]; + tensor var_7338_to_fp16 = const()[name = tensor("op_7338_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_299_cast_fp16 = layer_norm(axes = hidden_states_299_axes_0, beta = hidden_states_299_beta_0_to_fp16, epsilon = var_7338_to_fp16, gamma = hidden_states_299_gamma_0_to_fp16, x = inputs_217_cast_fp16)[name = tensor("hidden_states_299_cast_fp16")]; + tensor var_7353 = const()[name = tensor("op_7353"), val = tensor([1, 1])]; + tensor var_7355 = const()[name = tensor("op_7355"), val = tensor([1, 1])]; + tensor q_145_pad_type_0 = const()[name = tensor("q_145_pad_type_0"), val = tensor("custom")]; + tensor q_145_pad_0 = const()[name = tensor("q_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1023524160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1024753024))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_145_cast_fp16 = conv(dilations = var_7355, groups = var_6865, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7353, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_299_cast_fp16)[name = tensor("q_145_cast_fp16")]; + tensor var_7359 = const()[name = tensor("op_7359"), val = tensor([1, 1])]; + tensor var_7361 = const()[name = tensor("op_7361"), val = tensor([1, 1])]; + tensor k_145_pad_type_0 = const()[name = tensor("k_145_pad_type_0"), val = tensor("custom")]; + tensor k_145_pad_0 = const()[name = tensor("k_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1024753216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025982080))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_145_cast_fp16 = conv(dilations = var_7361, groups = var_6865, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7359, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_299_cast_fp16)[name = tensor("k_145_cast_fp16")]; + tensor var_7365 = const()[name = tensor("op_7365"), val = tensor([1, 1])]; + tensor var_7367 = const()[name = tensor("op_7367"), val = tensor([1, 1])]; + tensor v_145_pad_type_0 = const()[name = tensor("v_145_pad_type_0"), val = tensor("custom")]; + tensor v_145_pad_0 = const()[name = tensor("v_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025982272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027211136))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_145_cast_fp16 = conv(dilations = var_7367, groups = var_6865, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7365, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_299_cast_fp16)[name = tensor("v_145_cast_fp16")]; + tensor var_7371 = const()[name = tensor("op_7371"), val = tensor([1, 20, 64, -1])]; + tensor var_7372_cast_fp16 = reshape(shape = var_7371, x = q_145_cast_fp16)[name = tensor("op_7372_cast_fp16")]; + tensor var_7373 = const()[name = tensor("op_7373"), val = tensor([1, 20, 64, -1])]; + tensor var_7374_cast_fp16 = reshape(shape = var_7373, x = k_145_cast_fp16)[name = tensor("op_7374_cast_fp16")]; + tensor var_7375 = const()[name = tensor("op_7375"), val = tensor([1, 20, 64, -1])]; + tensor var_7376_cast_fp16 = reshape(shape = var_7375, x = v_145_cast_fp16)[name = tensor("op_7376_cast_fp16")]; + tensor attn_weights_289_transpose_x_0 = const()[name = tensor("attn_weights_289_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_289_transpose_y_0 = const()[name = tensor("attn_weights_289_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7372_cast_fp16, y = var_7374_cast_fp16)[name = tensor("attn_weights_289_cast_fp16")]; + tensor attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_291_cast_fp16")]; + tensor var_7380_cast_fp16 = softmax(axis = var_6849, x = attn_weights_291_cast_fp16)[name = tensor("op_7380_cast_fp16")]; + tensor attn_145_transpose_x_0 = const()[name = tensor("attn_145_transpose_x_0"), val = tensor(false)]; + tensor attn_145_transpose_y_0 = const()[name = tensor("attn_145_transpose_y_0"), val = tensor(true)]; + tensor attn_145_cast_fp16 = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_7376_cast_fp16, y = var_7380_cast_fp16)[name = tensor("attn_145_cast_fp16")]; + tensor var_7384 = const()[name = tensor("op_7384"), val = tensor([1, 1280, 1, -1])]; + tensor input_455_cast_fp16 = reshape(shape = var_7384, x = attn_145_cast_fp16)[name = tensor("input_455_cast_fp16")]; + tensor var_7389 = const()[name = tensor("op_7389"), val = tensor([1, 1])]; + tensor var_7391 = const()[name = tensor("op_7391"), val = tensor([1, 1])]; + tensor var_7393_pad_type_0 = const()[name = tensor("op_7393_pad_type_0"), val = tensor("custom")]; + tensor var_7393_pad_0 = const()[name = tensor("op_7393_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027211328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028440192))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028440384)))]; + tensor var_7393_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_7391, groups = var_6865, pad = var_7393_pad_0, pad_type = var_7393_pad_type_0, strides = var_7389, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_455_cast_fp16)[name = tensor("op_7393_cast_fp16")]; + tensor inputs_219_cast_fp16 = add(x = var_7393_cast_fp16, y = inputs_217_cast_fp16)[name = tensor("inputs_219_cast_fp16")]; + tensor hidden_states_301_axes_0 = const()[name = tensor("hidden_states_301_axes_0"), val = tensor([1])]; + tensor hidden_states_301_gamma_0_to_fp16 = const()[name = tensor("hidden_states_301_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028443008)))]; + tensor hidden_states_301_beta_0_to_fp16 = const()[name = tensor("hidden_states_301_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028445632)))]; + tensor var_7403_to_fp16 = const()[name = tensor("op_7403_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_301_cast_fp16 = layer_norm(axes = hidden_states_301_axes_0, beta = hidden_states_301_beta_0_to_fp16, epsilon = var_7403_to_fp16, gamma = hidden_states_301_gamma_0_to_fp16, x = inputs_219_cast_fp16)[name = tensor("hidden_states_301_cast_fp16")]; + tensor var_7418 = const()[name = tensor("op_7418"), val = tensor([1, 1])]; + tensor var_7420 = const()[name = tensor("op_7420"), val = tensor([1, 1])]; + tensor q_147_pad_type_0 = const()[name = tensor("q_147_pad_type_0"), val = tensor("custom")]; + tensor q_147_pad_0 = const()[name = tensor("q_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1028448256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1029677120))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_147_cast_fp16 = conv(dilations = var_7420, groups = var_6865, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_7418, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_301_cast_fp16)[name = tensor("q_147_cast_fp16")]; + tensor var_7424 = const()[name = tensor("op_7424"), val = tensor([1, 1])]; + tensor var_7426 = const()[name = tensor("op_7426"), val = tensor([1, 1])]; + tensor k_147_pad_type_0 = const()[name = tensor("k_147_pad_type_0"), val = tensor("custom")]; + tensor k_147_pad_0 = const()[name = tensor("k_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1029677312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031643456))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_147_cast_fp16 = conv(dilations = var_7426, groups = var_6865, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_7424, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_147_cast_fp16")]; + tensor var_7430 = const()[name = tensor("op_7430"), val = tensor([1, 1])]; + tensor var_7432 = const()[name = tensor("op_7432"), val = tensor([1, 1])]; + tensor v_147_pad_type_0 = const()[name = tensor("v_147_pad_type_0"), val = tensor("custom")]; + tensor v_147_pad_0 = const()[name = tensor("v_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031643648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1033609792))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_147_cast_fp16 = conv(dilations = var_7432, groups = var_6865, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_7430, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_147_cast_fp16")]; + tensor var_7436 = const()[name = tensor("op_7436"), val = tensor([1, 20, 64, -1])]; + tensor var_7437_cast_fp16 = reshape(shape = var_7436, x = q_147_cast_fp16)[name = tensor("op_7437_cast_fp16")]; + tensor var_7438 = const()[name = tensor("op_7438"), val = tensor([1, 20, 64, -1])]; + tensor var_7439_cast_fp16 = reshape(shape = var_7438, x = k_147_cast_fp16)[name = tensor("op_7439_cast_fp16")]; + tensor var_7440 = const()[name = tensor("op_7440"), val = tensor([1, 20, 64, -1])]; + tensor var_7441_cast_fp16 = reshape(shape = var_7440, x = v_147_cast_fp16)[name = tensor("op_7441_cast_fp16")]; + tensor attn_weights_293_transpose_x_0 = const()[name = tensor("attn_weights_293_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_293_transpose_y_0 = const()[name = tensor("attn_weights_293_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_293_cast_fp16 = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_7437_cast_fp16, y = var_7439_cast_fp16)[name = tensor("attn_weights_293_cast_fp16")]; + tensor attn_weights_295_cast_fp16 = mul(x = attn_weights_293_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_295_cast_fp16")]; + tensor var_7445_cast_fp16 = softmax(axis = var_6849, x = attn_weights_295_cast_fp16)[name = tensor("op_7445_cast_fp16")]; + tensor attn_147_transpose_x_0 = const()[name = tensor("attn_147_transpose_x_0"), val = tensor(false)]; + tensor attn_147_transpose_y_0 = const()[name = tensor("attn_147_transpose_y_0"), val = tensor(true)]; + tensor attn_147_cast_fp16 = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_7441_cast_fp16, y = var_7445_cast_fp16)[name = tensor("attn_147_cast_fp16")]; + tensor var_7449 = const()[name = tensor("op_7449"), val = tensor([1, 1280, 1, -1])]; + tensor input_457_cast_fp16 = reshape(shape = var_7449, x = attn_147_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor var_7454 = const()[name = tensor("op_7454"), val = tensor([1, 1])]; + tensor var_7456 = const()[name = tensor("op_7456"), val = tensor([1, 1])]; + tensor var_7458_pad_type_0 = const()[name = tensor("op_7458_pad_type_0"), val = tensor("custom")]; + tensor var_7458_pad_0 = const()[name = tensor("op_7458_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1033609984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034838848))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034839040)))]; + tensor var_7458_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_7456, groups = var_6865, pad = var_7458_pad_0, pad_type = var_7458_pad_type_0, strides = var_7454, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_457_cast_fp16)[name = tensor("op_7458_cast_fp16")]; + tensor inputs_221_cast_fp16 = add(x = var_7458_cast_fp16, y = inputs_219_cast_fp16)[name = tensor("inputs_221_cast_fp16")]; + tensor input_459_axes_0 = const()[name = tensor("input_459_axes_0"), val = tensor([1])]; + tensor input_459_gamma_0_to_fp16 = const()[name = tensor("input_459_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034841664)))]; + tensor input_459_beta_0_to_fp16 = const()[name = tensor("input_459_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034844288)))]; + tensor var_7468_to_fp16 = const()[name = tensor("op_7468_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_459_cast_fp16 = layer_norm(axes = input_459_axes_0, beta = input_459_beta_0_to_fp16, epsilon = var_7468_to_fp16, gamma = input_459_gamma_0_to_fp16, x = inputs_221_cast_fp16)[name = tensor("input_459_cast_fp16")]; + tensor var_7484 = const()[name = tensor("op_7484"), val = tensor([1, 1])]; + tensor var_7486 = const()[name = tensor("op_7486"), val = tensor([1, 1])]; + tensor var_7488_pad_type_0 = const()[name = tensor("op_7488_pad_type_0"), val = tensor("custom")]; + tensor var_7488_pad_0 = const()[name = tensor("op_7488_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034846912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044677376))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044677568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044685312))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7488_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7486, groups = var_6865, pad = var_7488_pad_0, pad_type = var_7488_pad_type_0, strides = var_7484, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = tensor("op_7488_cast_fp16")]; + tensor var_7489_split_sizes_0 = const()[name = tensor("op_7489_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7489_axis_0 = const()[name = tensor("op_7489_axis_0"), val = tensor(1)]; + tensor var_7489_cast_fp16_0, tensor var_7489_cast_fp16_1 = split(axis = var_7489_axis_0, split_sizes = var_7489_split_sizes_0, x = var_7488_cast_fp16)[name = tensor("op_7489_cast_fp16")]; + tensor var_7491_mode_0 = const()[name = tensor("op_7491_mode_0"), val = tensor("EXACT")]; + tensor var_7491_cast_fp16 = gelu(mode = var_7491_mode_0, x = var_7489_cast_fp16_1)[name = tensor("op_7491_cast_fp16")]; + tensor input_461_cast_fp16 = mul(x = var_7489_cast_fp16_0, y = var_7491_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor var_7495 = const()[name = tensor("op_7495"), val = tensor([1, 1])]; + tensor var_7497 = const()[name = tensor("op_7497"), val = tensor([1, 1])]; + tensor var_7499_pad_type_0 = const()[name = tensor("op_7499_pad_type_0"), val = tensor("custom")]; + tensor var_7499_pad_0 = const()[name = tensor("op_7499_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044685504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049600768))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049600960)))]; + tensor var_7499_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_7497, groups = var_6865, pad = var_7499_pad_0, pad_type = var_7499_pad_type_0, strides = var_7495, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_461_cast_fp16)[name = tensor("op_7499_cast_fp16")]; + tensor inputs_223_cast_fp16 = add(x = var_7499_cast_fp16, y = inputs_221_cast_fp16)[name = tensor("inputs_223_cast_fp16")]; + tensor hidden_states_305_axes_0 = const()[name = tensor("hidden_states_305_axes_0"), val = tensor([1])]; + tensor hidden_states_305_gamma_0_to_fp16 = const()[name = tensor("hidden_states_305_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049603584)))]; + tensor hidden_states_305_beta_0_to_fp16 = const()[name = tensor("hidden_states_305_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049606208)))]; + tensor var_7515_to_fp16 = const()[name = tensor("op_7515_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_305_cast_fp16 = layer_norm(axes = hidden_states_305_axes_0, beta = hidden_states_305_beta_0_to_fp16, epsilon = var_7515_to_fp16, gamma = hidden_states_305_gamma_0_to_fp16, x = inputs_223_cast_fp16)[name = tensor("hidden_states_305_cast_fp16")]; + tensor var_7530 = const()[name = tensor("op_7530"), val = tensor([1, 1])]; + tensor var_7532 = const()[name = tensor("op_7532"), val = tensor([1, 1])]; + tensor q_149_pad_type_0 = const()[name = tensor("q_149_pad_type_0"), val = tensor("custom")]; + tensor q_149_pad_0 = const()[name = tensor("q_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1049608832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050837696))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_149_cast_fp16 = conv(dilations = var_7532, groups = var_6865, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_7530, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_305_cast_fp16)[name = tensor("q_149_cast_fp16")]; + tensor var_7536 = const()[name = tensor("op_7536"), val = tensor([1, 1])]; + tensor var_7538 = const()[name = tensor("op_7538"), val = tensor([1, 1])]; + tensor k_149_pad_type_0 = const()[name = tensor("k_149_pad_type_0"), val = tensor("custom")]; + tensor k_149_pad_0 = const()[name = tensor("k_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050837888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052066752))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_149_cast_fp16 = conv(dilations = var_7538, groups = var_6865, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_7536, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_305_cast_fp16)[name = tensor("k_149_cast_fp16")]; + tensor var_7542 = const()[name = tensor("op_7542"), val = tensor([1, 1])]; + tensor var_7544 = const()[name = tensor("op_7544"), val = tensor([1, 1])]; + tensor v_149_pad_type_0 = const()[name = tensor("v_149_pad_type_0"), val = tensor("custom")]; + tensor v_149_pad_0 = const()[name = tensor("v_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052066944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1053295808))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_149_cast_fp16 = conv(dilations = var_7544, groups = var_6865, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_7542, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_305_cast_fp16)[name = tensor("v_149_cast_fp16")]; + tensor var_7548 = const()[name = tensor("op_7548"), val = tensor([1, 20, 64, -1])]; + tensor var_7549_cast_fp16 = reshape(shape = var_7548, x = q_149_cast_fp16)[name = tensor("op_7549_cast_fp16")]; + tensor var_7550 = const()[name = tensor("op_7550"), val = tensor([1, 20, 64, -1])]; + tensor var_7551_cast_fp16 = reshape(shape = var_7550, x = k_149_cast_fp16)[name = tensor("op_7551_cast_fp16")]; + tensor var_7552 = const()[name = tensor("op_7552"), val = tensor([1, 20, 64, -1])]; + tensor var_7553_cast_fp16 = reshape(shape = var_7552, x = v_149_cast_fp16)[name = tensor("op_7553_cast_fp16")]; + tensor attn_weights_297_transpose_x_0 = const()[name = tensor("attn_weights_297_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_297_transpose_y_0 = const()[name = tensor("attn_weights_297_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_7549_cast_fp16, y = var_7551_cast_fp16)[name = tensor("attn_weights_297_cast_fp16")]; + tensor attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_299_cast_fp16")]; + tensor var_7557_cast_fp16 = softmax(axis = var_6849, x = attn_weights_299_cast_fp16)[name = tensor("op_7557_cast_fp16")]; + tensor attn_149_transpose_x_0 = const()[name = tensor("attn_149_transpose_x_0"), val = tensor(false)]; + tensor attn_149_transpose_y_0 = const()[name = tensor("attn_149_transpose_y_0"), val = tensor(true)]; + tensor attn_149_cast_fp16 = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_7553_cast_fp16, y = var_7557_cast_fp16)[name = tensor("attn_149_cast_fp16")]; + tensor var_7561 = const()[name = tensor("op_7561"), val = tensor([1, 1280, 1, -1])]; + tensor input_463_cast_fp16 = reshape(shape = var_7561, x = attn_149_cast_fp16)[name = tensor("input_463_cast_fp16")]; + tensor var_7566 = const()[name = tensor("op_7566"), val = tensor([1, 1])]; + tensor var_7568 = const()[name = tensor("op_7568"), val = tensor([1, 1])]; + tensor var_7570_pad_type_0 = const()[name = tensor("op_7570_pad_type_0"), val = tensor("custom")]; + tensor var_7570_pad_0 = const()[name = tensor("op_7570_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1053296000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054524864))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054525056)))]; + tensor var_7570_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_7568, groups = var_6865, pad = var_7570_pad_0, pad_type = var_7570_pad_type_0, strides = var_7566, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_463_cast_fp16)[name = tensor("op_7570_cast_fp16")]; + tensor inputs_225_cast_fp16 = add(x = var_7570_cast_fp16, y = inputs_223_cast_fp16)[name = tensor("inputs_225_cast_fp16")]; + tensor hidden_states_307_axes_0 = const()[name = tensor("hidden_states_307_axes_0"), val = tensor([1])]; + tensor hidden_states_307_gamma_0_to_fp16 = const()[name = tensor("hidden_states_307_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054527680)))]; + tensor hidden_states_307_beta_0_to_fp16 = const()[name = tensor("hidden_states_307_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054530304)))]; + tensor var_7580_to_fp16 = const()[name = tensor("op_7580_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_307_cast_fp16 = layer_norm(axes = hidden_states_307_axes_0, beta = hidden_states_307_beta_0_to_fp16, epsilon = var_7580_to_fp16, gamma = hidden_states_307_gamma_0_to_fp16, x = inputs_225_cast_fp16)[name = tensor("hidden_states_307_cast_fp16")]; + tensor var_7595 = const()[name = tensor("op_7595"), val = tensor([1, 1])]; + tensor var_7597 = const()[name = tensor("op_7597"), val = tensor([1, 1])]; + tensor q_151_pad_type_0 = const()[name = tensor("q_151_pad_type_0"), val = tensor("custom")]; + tensor q_151_pad_0 = const()[name = tensor("q_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054532928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1055761792))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_151_cast_fp16 = conv(dilations = var_7597, groups = var_6865, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_7595, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast_fp16)[name = tensor("q_151_cast_fp16")]; + tensor var_7601 = const()[name = tensor("op_7601"), val = tensor([1, 1])]; + tensor var_7603 = const()[name = tensor("op_7603"), val = tensor([1, 1])]; + tensor k_151_pad_type_0 = const()[name = tensor("k_151_pad_type_0"), val = tensor("custom")]; + tensor k_151_pad_0 = const()[name = tensor("k_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1055761984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057728128))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_151_cast_fp16 = conv(dilations = var_7603, groups = var_6865, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_7601, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_151_cast_fp16")]; + tensor var_7607 = const()[name = tensor("op_7607"), val = tensor([1, 1])]; + tensor var_7609 = const()[name = tensor("op_7609"), val = tensor([1, 1])]; + tensor v_151_pad_type_0 = const()[name = tensor("v_151_pad_type_0"), val = tensor("custom")]; + tensor v_151_pad_0 = const()[name = tensor("v_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057728320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1059694464))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_151_cast_fp16 = conv(dilations = var_7609, groups = var_6865, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_7607, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_151_cast_fp16")]; + tensor var_7613 = const()[name = tensor("op_7613"), val = tensor([1, 20, 64, -1])]; + tensor var_7614_cast_fp16 = reshape(shape = var_7613, x = q_151_cast_fp16)[name = tensor("op_7614_cast_fp16")]; + tensor var_7615 = const()[name = tensor("op_7615"), val = tensor([1, 20, 64, -1])]; + tensor var_7616_cast_fp16 = reshape(shape = var_7615, x = k_151_cast_fp16)[name = tensor("op_7616_cast_fp16")]; + tensor var_7617 = const()[name = tensor("op_7617"), val = tensor([1, 20, 64, -1])]; + tensor var_7618_cast_fp16 = reshape(shape = var_7617, x = v_151_cast_fp16)[name = tensor("op_7618_cast_fp16")]; + tensor attn_weights_301_transpose_x_0 = const()[name = tensor("attn_weights_301_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_301_transpose_y_0 = const()[name = tensor("attn_weights_301_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_301_cast_fp16 = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_7614_cast_fp16, y = var_7616_cast_fp16)[name = tensor("attn_weights_301_cast_fp16")]; + tensor attn_weights_303_cast_fp16 = mul(x = attn_weights_301_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_303_cast_fp16")]; + tensor var_7622_cast_fp16 = softmax(axis = var_6849, x = attn_weights_303_cast_fp16)[name = tensor("op_7622_cast_fp16")]; + tensor attn_151_transpose_x_0 = const()[name = tensor("attn_151_transpose_x_0"), val = tensor(false)]; + tensor attn_151_transpose_y_0 = const()[name = tensor("attn_151_transpose_y_0"), val = tensor(true)]; + tensor attn_151_cast_fp16 = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_7618_cast_fp16, y = var_7622_cast_fp16)[name = tensor("attn_151_cast_fp16")]; + tensor var_7626 = const()[name = tensor("op_7626"), val = tensor([1, 1280, 1, -1])]; + tensor input_465_cast_fp16 = reshape(shape = var_7626, x = attn_151_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor var_7631 = const()[name = tensor("op_7631"), val = tensor([1, 1])]; + tensor var_7633 = const()[name = tensor("op_7633"), val = tensor([1, 1])]; + tensor var_7635_pad_type_0 = const()[name = tensor("op_7635_pad_type_0"), val = tensor("custom")]; + tensor var_7635_pad_0 = const()[name = tensor("op_7635_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1059694656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060923520))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060923712)))]; + tensor var_7635_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_7633, groups = var_6865, pad = var_7635_pad_0, pad_type = var_7635_pad_type_0, strides = var_7631, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_465_cast_fp16)[name = tensor("op_7635_cast_fp16")]; + tensor inputs_227_cast_fp16 = add(x = var_7635_cast_fp16, y = inputs_225_cast_fp16)[name = tensor("inputs_227_cast_fp16")]; + tensor input_467_axes_0 = const()[name = tensor("input_467_axes_0"), val = tensor([1])]; + tensor input_467_gamma_0_to_fp16 = const()[name = tensor("input_467_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060926336)))]; + tensor input_467_beta_0_to_fp16 = const()[name = tensor("input_467_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060928960)))]; + tensor var_7645_to_fp16 = const()[name = tensor("op_7645_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_467_cast_fp16 = layer_norm(axes = input_467_axes_0, beta = input_467_beta_0_to_fp16, epsilon = var_7645_to_fp16, gamma = input_467_gamma_0_to_fp16, x = inputs_227_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor var_7661 = const()[name = tensor("op_7661"), val = tensor([1, 1])]; + tensor var_7663 = const()[name = tensor("op_7663"), val = tensor([1, 1])]; + tensor var_7665_pad_type_0 = const()[name = tensor("op_7665_pad_type_0"), val = tensor("custom")]; + tensor var_7665_pad_0 = const()[name = tensor("op_7665_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060931584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070762048))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070762240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070769984))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7665_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7663, groups = var_6865, pad = var_7665_pad_0, pad_type = var_7665_pad_type_0, strides = var_7661, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_467_cast_fp16)[name = tensor("op_7665_cast_fp16")]; + tensor var_7666_split_sizes_0 = const()[name = tensor("op_7666_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7666_axis_0 = const()[name = tensor("op_7666_axis_0"), val = tensor(1)]; + tensor var_7666_cast_fp16_0, tensor var_7666_cast_fp16_1 = split(axis = var_7666_axis_0, split_sizes = var_7666_split_sizes_0, x = var_7665_cast_fp16)[name = tensor("op_7666_cast_fp16")]; + tensor var_7668_mode_0 = const()[name = tensor("op_7668_mode_0"), val = tensor("EXACT")]; + tensor var_7668_cast_fp16 = gelu(mode = var_7668_mode_0, x = var_7666_cast_fp16_1)[name = tensor("op_7668_cast_fp16")]; + tensor input_469_cast_fp16 = mul(x = var_7666_cast_fp16_0, y = var_7668_cast_fp16)[name = tensor("input_469_cast_fp16")]; + tensor var_7672 = const()[name = tensor("op_7672"), val = tensor([1, 1])]; + tensor var_7674 = const()[name = tensor("op_7674"), val = tensor([1, 1])]; + tensor var_7676_pad_type_0 = const()[name = tensor("op_7676_pad_type_0"), val = tensor("custom")]; + tensor var_7676_pad_0 = const()[name = tensor("op_7676_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1070770176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075685440))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075685632)))]; + tensor var_7676_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7674, groups = var_6865, pad = var_7676_pad_0, pad_type = var_7676_pad_type_0, strides = var_7672, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_469_cast_fp16)[name = tensor("op_7676_cast_fp16")]; + tensor inputs_229_cast_fp16 = add(x = var_7676_cast_fp16, y = inputs_227_cast_fp16)[name = tensor("inputs_229_cast_fp16")]; + tensor hidden_states_311_axes_0 = const()[name = tensor("hidden_states_311_axes_0"), val = tensor([1])]; + tensor hidden_states_311_gamma_0_to_fp16 = const()[name = tensor("hidden_states_311_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075688256)))]; + tensor hidden_states_311_beta_0_to_fp16 = const()[name = tensor("hidden_states_311_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075690880)))]; + tensor var_7692_to_fp16 = const()[name = tensor("op_7692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_311_cast_fp16 = layer_norm(axes = hidden_states_311_axes_0, beta = hidden_states_311_beta_0_to_fp16, epsilon = var_7692_to_fp16, gamma = hidden_states_311_gamma_0_to_fp16, x = inputs_229_cast_fp16)[name = tensor("hidden_states_311_cast_fp16")]; + tensor var_7707 = const()[name = tensor("op_7707"), val = tensor([1, 1])]; + tensor var_7709 = const()[name = tensor("op_7709"), val = tensor([1, 1])]; + tensor q_153_pad_type_0 = const()[name = tensor("q_153_pad_type_0"), val = tensor("custom")]; + tensor q_153_pad_0 = const()[name = tensor("q_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1075693504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1076922368))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_153_cast_fp16 = conv(dilations = var_7709, groups = var_6865, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_7707, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_311_cast_fp16)[name = tensor("q_153_cast_fp16")]; + tensor var_7713 = const()[name = tensor("op_7713"), val = tensor([1, 1])]; + tensor var_7715 = const()[name = tensor("op_7715"), val = tensor([1, 1])]; + tensor k_153_pad_type_0 = const()[name = tensor("k_153_pad_type_0"), val = tensor("custom")]; + tensor k_153_pad_0 = const()[name = tensor("k_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1076922560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1078151424))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_153_cast_fp16 = conv(dilations = var_7715, groups = var_6865, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_7713, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_311_cast_fp16)[name = tensor("k_153_cast_fp16")]; + tensor var_7719 = const()[name = tensor("op_7719"), val = tensor([1, 1])]; + tensor var_7721 = const()[name = tensor("op_7721"), val = tensor([1, 1])]; + tensor v_153_pad_type_0 = const()[name = tensor("v_153_pad_type_0"), val = tensor("custom")]; + tensor v_153_pad_0 = const()[name = tensor("v_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1078151616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1079380480))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_153_cast_fp16 = conv(dilations = var_7721, groups = var_6865, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_7719, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_311_cast_fp16)[name = tensor("v_153_cast_fp16")]; + tensor var_7725 = const()[name = tensor("op_7725"), val = tensor([1, 20, 64, -1])]; + tensor var_7726_cast_fp16 = reshape(shape = var_7725, x = q_153_cast_fp16)[name = tensor("op_7726_cast_fp16")]; + tensor var_7727 = const()[name = tensor("op_7727"), val = tensor([1, 20, 64, -1])]; + tensor var_7728_cast_fp16 = reshape(shape = var_7727, x = k_153_cast_fp16)[name = tensor("op_7728_cast_fp16")]; + tensor var_7729 = const()[name = tensor("op_7729"), val = tensor([1, 20, 64, -1])]; + tensor var_7730_cast_fp16 = reshape(shape = var_7729, x = v_153_cast_fp16)[name = tensor("op_7730_cast_fp16")]; + tensor attn_weights_305_transpose_x_0 = const()[name = tensor("attn_weights_305_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_305_transpose_y_0 = const()[name = tensor("attn_weights_305_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7726_cast_fp16, y = var_7728_cast_fp16)[name = tensor("attn_weights_305_cast_fp16")]; + tensor attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_307_cast_fp16")]; + tensor var_7734_cast_fp16 = softmax(axis = var_6849, x = attn_weights_307_cast_fp16)[name = tensor("op_7734_cast_fp16")]; + tensor attn_153_transpose_x_0 = const()[name = tensor("attn_153_transpose_x_0"), val = tensor(false)]; + tensor attn_153_transpose_y_0 = const()[name = tensor("attn_153_transpose_y_0"), val = tensor(true)]; + tensor attn_153_cast_fp16 = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_7730_cast_fp16, y = var_7734_cast_fp16)[name = tensor("attn_153_cast_fp16")]; + tensor var_7738 = const()[name = tensor("op_7738"), val = tensor([1, 1280, 1, -1])]; + tensor input_471_cast_fp16 = reshape(shape = var_7738, x = attn_153_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor var_7743 = const()[name = tensor("op_7743"), val = tensor([1, 1])]; + tensor var_7745 = const()[name = tensor("op_7745"), val = tensor([1, 1])]; + tensor var_7747_pad_type_0 = const()[name = tensor("op_7747_pad_type_0"), val = tensor("custom")]; + tensor var_7747_pad_0 = const()[name = tensor("op_7747_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1079380672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080609536))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080609728)))]; + tensor var_7747_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_7745, groups = var_6865, pad = var_7747_pad_0, pad_type = var_7747_pad_type_0, strides = var_7743, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_471_cast_fp16)[name = tensor("op_7747_cast_fp16")]; + tensor inputs_231_cast_fp16 = add(x = var_7747_cast_fp16, y = inputs_229_cast_fp16)[name = tensor("inputs_231_cast_fp16")]; + tensor hidden_states_313_axes_0 = const()[name = tensor("hidden_states_313_axes_0"), val = tensor([1])]; + tensor hidden_states_313_gamma_0_to_fp16 = const()[name = tensor("hidden_states_313_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080612352)))]; + tensor hidden_states_313_beta_0_to_fp16 = const()[name = tensor("hidden_states_313_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080614976)))]; + tensor var_7757_to_fp16 = const()[name = tensor("op_7757_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_313_cast_fp16 = layer_norm(axes = hidden_states_313_axes_0, beta = hidden_states_313_beta_0_to_fp16, epsilon = var_7757_to_fp16, gamma = hidden_states_313_gamma_0_to_fp16, x = inputs_231_cast_fp16)[name = tensor("hidden_states_313_cast_fp16")]; + tensor var_7772 = const()[name = tensor("op_7772"), val = tensor([1, 1])]; + tensor var_7774 = const()[name = tensor("op_7774"), val = tensor([1, 1])]; + tensor q_155_pad_type_0 = const()[name = tensor("q_155_pad_type_0"), val = tensor("custom")]; + tensor q_155_pad_0 = const()[name = tensor("q_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1080617600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081846464))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_155_cast_fp16 = conv(dilations = var_7774, groups = var_6865, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_7772, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_313_cast_fp16)[name = tensor("q_155_cast_fp16")]; + tensor var_7778 = const()[name = tensor("op_7778"), val = tensor([1, 1])]; + tensor var_7780 = const()[name = tensor("op_7780"), val = tensor([1, 1])]; + tensor k_155_pad_type_0 = const()[name = tensor("k_155_pad_type_0"), val = tensor("custom")]; + tensor k_155_pad_0 = const()[name = tensor("k_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081846656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1083812800))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_155_cast_fp16 = conv(dilations = var_7780, groups = var_6865, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_7778, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_155_cast_fp16")]; + tensor var_7784 = const()[name = tensor("op_7784"), val = tensor([1, 1])]; + tensor var_7786 = const()[name = tensor("op_7786"), val = tensor([1, 1])]; + tensor v_155_pad_type_0 = const()[name = tensor("v_155_pad_type_0"), val = tensor("custom")]; + tensor v_155_pad_0 = const()[name = tensor("v_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1083812992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1085779136))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_155_cast_fp16 = conv(dilations = var_7786, groups = var_6865, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_7784, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_155_cast_fp16")]; + tensor var_7790 = const()[name = tensor("op_7790"), val = tensor([1, 20, 64, -1])]; + tensor var_7791_cast_fp16 = reshape(shape = var_7790, x = q_155_cast_fp16)[name = tensor("op_7791_cast_fp16")]; + tensor var_7792 = const()[name = tensor("op_7792"), val = tensor([1, 20, 64, -1])]; + tensor var_7793_cast_fp16 = reshape(shape = var_7792, x = k_155_cast_fp16)[name = tensor("op_7793_cast_fp16")]; + tensor var_7794 = const()[name = tensor("op_7794"), val = tensor([1, 20, 64, -1])]; + tensor var_7795_cast_fp16 = reshape(shape = var_7794, x = v_155_cast_fp16)[name = tensor("op_7795_cast_fp16")]; + tensor attn_weights_309_transpose_x_0 = const()[name = tensor("attn_weights_309_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_309_transpose_y_0 = const()[name = tensor("attn_weights_309_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_309_cast_fp16 = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_7791_cast_fp16, y = var_7793_cast_fp16)[name = tensor("attn_weights_309_cast_fp16")]; + tensor attn_weights_311_cast_fp16 = mul(x = attn_weights_309_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_311_cast_fp16")]; + tensor var_7799_cast_fp16 = softmax(axis = var_6849, x = attn_weights_311_cast_fp16)[name = tensor("op_7799_cast_fp16")]; + tensor attn_155_transpose_x_0 = const()[name = tensor("attn_155_transpose_x_0"), val = tensor(false)]; + tensor attn_155_transpose_y_0 = const()[name = tensor("attn_155_transpose_y_0"), val = tensor(true)]; + tensor attn_155_cast_fp16 = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_7795_cast_fp16, y = var_7799_cast_fp16)[name = tensor("attn_155_cast_fp16")]; + tensor var_7803 = const()[name = tensor("op_7803"), val = tensor([1, 1280, 1, -1])]; + tensor input_473_cast_fp16 = reshape(shape = var_7803, x = attn_155_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor var_7808 = const()[name = tensor("op_7808"), val = tensor([1, 1])]; + tensor var_7810 = const()[name = tensor("op_7810"), val = tensor([1, 1])]; + tensor var_7812_pad_type_0 = const()[name = tensor("op_7812_pad_type_0"), val = tensor("custom")]; + tensor var_7812_pad_0 = const()[name = tensor("op_7812_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1085779328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087008192))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087008384)))]; + tensor var_7812_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_7810, groups = var_6865, pad = var_7812_pad_0, pad_type = var_7812_pad_type_0, strides = var_7808, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_473_cast_fp16)[name = tensor("op_7812_cast_fp16")]; + tensor inputs_233_cast_fp16 = add(x = var_7812_cast_fp16, y = inputs_231_cast_fp16)[name = tensor("inputs_233_cast_fp16")]; + tensor input_475_axes_0 = const()[name = tensor("input_475_axes_0"), val = tensor([1])]; + tensor input_475_gamma_0_to_fp16 = const()[name = tensor("input_475_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087011008)))]; + tensor input_475_beta_0_to_fp16 = const()[name = tensor("input_475_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087013632)))]; + tensor var_7822_to_fp16 = const()[name = tensor("op_7822_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = input_475_beta_0_to_fp16, epsilon = var_7822_to_fp16, gamma = input_475_gamma_0_to_fp16, x = inputs_233_cast_fp16)[name = tensor("input_475_cast_fp16")]; + tensor var_7838 = const()[name = tensor("op_7838"), val = tensor([1, 1])]; + tensor var_7840 = const()[name = tensor("op_7840"), val = tensor([1, 1])]; + tensor var_7842_pad_type_0 = const()[name = tensor("op_7842_pad_type_0"), val = tensor("custom")]; + tensor var_7842_pad_0 = const()[name = tensor("op_7842_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1087016256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096846720))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096846912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096854656))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_7842_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_7840, groups = var_6865, pad = var_7842_pad_0, pad_type = var_7842_pad_type_0, strides = var_7838, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_475_cast_fp16)[name = tensor("op_7842_cast_fp16")]; + tensor var_7843_split_sizes_0 = const()[name = tensor("op_7843_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7843_axis_0 = const()[name = tensor("op_7843_axis_0"), val = tensor(1)]; + tensor var_7843_cast_fp16_0, tensor var_7843_cast_fp16_1 = split(axis = var_7843_axis_0, split_sizes = var_7843_split_sizes_0, x = var_7842_cast_fp16)[name = tensor("op_7843_cast_fp16")]; + tensor var_7845_mode_0 = const()[name = tensor("op_7845_mode_0"), val = tensor("EXACT")]; + tensor var_7845_cast_fp16 = gelu(mode = var_7845_mode_0, x = var_7843_cast_fp16_1)[name = tensor("op_7845_cast_fp16")]; + tensor input_477_cast_fp16 = mul(x = var_7843_cast_fp16_0, y = var_7845_cast_fp16)[name = tensor("input_477_cast_fp16")]; + tensor var_7849 = const()[name = tensor("op_7849"), val = tensor([1, 1])]; + tensor var_7851 = const()[name = tensor("op_7851"), val = tensor([1, 1])]; + tensor var_7853_pad_type_0 = const()[name = tensor("op_7853_pad_type_0"), val = tensor("custom")]; + tensor var_7853_pad_0 = const()[name = tensor("op_7853_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1096854848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101770112))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101770304)))]; + tensor var_7853_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_7851, groups = var_6865, pad = var_7853_pad_0, pad_type = var_7853_pad_type_0, strides = var_7849, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_477_cast_fp16)[name = tensor("op_7853_cast_fp16")]; + tensor inputs_235_cast_fp16 = add(x = var_7853_cast_fp16, y = inputs_233_cast_fp16)[name = tensor("inputs_235_cast_fp16")]; + tensor hidden_states_317_axes_0 = const()[name = tensor("hidden_states_317_axes_0"), val = tensor([1])]; + tensor hidden_states_317_gamma_0_to_fp16 = const()[name = tensor("hidden_states_317_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101772928)))]; + tensor hidden_states_317_beta_0_to_fp16 = const()[name = tensor("hidden_states_317_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101775552)))]; + tensor var_7869_to_fp16 = const()[name = tensor("op_7869_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_317_cast_fp16 = layer_norm(axes = hidden_states_317_axes_0, beta = hidden_states_317_beta_0_to_fp16, epsilon = var_7869_to_fp16, gamma = hidden_states_317_gamma_0_to_fp16, x = inputs_235_cast_fp16)[name = tensor("hidden_states_317_cast_fp16")]; + tensor var_7884 = const()[name = tensor("op_7884"), val = tensor([1, 1])]; + tensor var_7886 = const()[name = tensor("op_7886"), val = tensor([1, 1])]; + tensor q_157_pad_type_0 = const()[name = tensor("q_157_pad_type_0"), val = tensor("custom")]; + tensor q_157_pad_0 = const()[name = tensor("q_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101778176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1103007040))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_157_cast_fp16 = conv(dilations = var_7886, groups = var_6865, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_7884, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_317_cast_fp16)[name = tensor("q_157_cast_fp16")]; + tensor var_7890 = const()[name = tensor("op_7890"), val = tensor([1, 1])]; + tensor var_7892 = const()[name = tensor("op_7892"), val = tensor([1, 1])]; + tensor k_157_pad_type_0 = const()[name = tensor("k_157_pad_type_0"), val = tensor("custom")]; + tensor k_157_pad_0 = const()[name = tensor("k_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1103007232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1104236096))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_157_cast_fp16 = conv(dilations = var_7892, groups = var_6865, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_7890, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_317_cast_fp16)[name = tensor("k_157_cast_fp16")]; + tensor var_7896 = const()[name = tensor("op_7896"), val = tensor([1, 1])]; + tensor var_7898 = const()[name = tensor("op_7898"), val = tensor([1, 1])]; + tensor v_157_pad_type_0 = const()[name = tensor("v_157_pad_type_0"), val = tensor("custom")]; + tensor v_157_pad_0 = const()[name = tensor("v_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1104236288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1105465152))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_157_cast_fp16 = conv(dilations = var_7898, groups = var_6865, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_7896, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_317_cast_fp16)[name = tensor("v_157_cast_fp16")]; + tensor var_7902 = const()[name = tensor("op_7902"), val = tensor([1, 20, 64, -1])]; + tensor var_7903_cast_fp16 = reshape(shape = var_7902, x = q_157_cast_fp16)[name = tensor("op_7903_cast_fp16")]; + tensor var_7904 = const()[name = tensor("op_7904"), val = tensor([1, 20, 64, -1])]; + tensor var_7905_cast_fp16 = reshape(shape = var_7904, x = k_157_cast_fp16)[name = tensor("op_7905_cast_fp16")]; + tensor var_7906 = const()[name = tensor("op_7906"), val = tensor([1, 20, 64, -1])]; + tensor var_7907_cast_fp16 = reshape(shape = var_7906, x = v_157_cast_fp16)[name = tensor("op_7907_cast_fp16")]; + tensor attn_weights_313_transpose_x_0 = const()[name = tensor("attn_weights_313_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_313_transpose_y_0 = const()[name = tensor("attn_weights_313_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7903_cast_fp16, y = var_7905_cast_fp16)[name = tensor("attn_weights_313_cast_fp16")]; + tensor attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_315_cast_fp16")]; + tensor var_7911_cast_fp16 = softmax(axis = var_6849, x = attn_weights_315_cast_fp16)[name = tensor("op_7911_cast_fp16")]; + tensor attn_157_transpose_x_0 = const()[name = tensor("attn_157_transpose_x_0"), val = tensor(false)]; + tensor attn_157_transpose_y_0 = const()[name = tensor("attn_157_transpose_y_0"), val = tensor(true)]; + tensor attn_157_cast_fp16 = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_7907_cast_fp16, y = var_7911_cast_fp16)[name = tensor("attn_157_cast_fp16")]; + tensor var_7915 = const()[name = tensor("op_7915"), val = tensor([1, 1280, 1, -1])]; + tensor input_479_cast_fp16 = reshape(shape = var_7915, x = attn_157_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor var_7920 = const()[name = tensor("op_7920"), val = tensor([1, 1])]; + tensor var_7922 = const()[name = tensor("op_7922"), val = tensor([1, 1])]; + tensor var_7924_pad_type_0 = const()[name = tensor("op_7924_pad_type_0"), val = tensor("custom")]; + tensor var_7924_pad_0 = const()[name = tensor("op_7924_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1105465344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106694208))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106694400)))]; + tensor var_7924_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_7922, groups = var_6865, pad = var_7924_pad_0, pad_type = var_7924_pad_type_0, strides = var_7920, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_479_cast_fp16)[name = tensor("op_7924_cast_fp16")]; + tensor inputs_237_cast_fp16 = add(x = var_7924_cast_fp16, y = inputs_235_cast_fp16)[name = tensor("inputs_237_cast_fp16")]; + tensor hidden_states_319_axes_0 = const()[name = tensor("hidden_states_319_axes_0"), val = tensor([1])]; + tensor hidden_states_319_gamma_0_to_fp16 = const()[name = tensor("hidden_states_319_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106697024)))]; + tensor hidden_states_319_beta_0_to_fp16 = const()[name = tensor("hidden_states_319_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106699648)))]; + tensor var_7934_to_fp16 = const()[name = tensor("op_7934_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_319_cast_fp16 = layer_norm(axes = hidden_states_319_axes_0, beta = hidden_states_319_beta_0_to_fp16, epsilon = var_7934_to_fp16, gamma = hidden_states_319_gamma_0_to_fp16, x = inputs_237_cast_fp16)[name = tensor("hidden_states_319_cast_fp16")]; + tensor var_7949 = const()[name = tensor("op_7949"), val = tensor([1, 1])]; + tensor var_7951 = const()[name = tensor("op_7951"), val = tensor([1, 1])]; + tensor q_159_pad_type_0 = const()[name = tensor("q_159_pad_type_0"), val = tensor("custom")]; + tensor q_159_pad_0 = const()[name = tensor("q_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1106702272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107931136))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_159_cast_fp16 = conv(dilations = var_7951, groups = var_6865, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_7949, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_319_cast_fp16)[name = tensor("q_159_cast_fp16")]; + tensor var_7955 = const()[name = tensor("op_7955"), val = tensor([1, 1])]; + tensor var_7957 = const()[name = tensor("op_7957"), val = tensor([1, 1])]; + tensor k_159_pad_type_0 = const()[name = tensor("k_159_pad_type_0"), val = tensor("custom")]; + tensor k_159_pad_0 = const()[name = tensor("k_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107931328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1109897472))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_159_cast_fp16 = conv(dilations = var_7957, groups = var_6865, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_7955, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_159_cast_fp16")]; + tensor var_7961 = const()[name = tensor("op_7961"), val = tensor([1, 1])]; + tensor var_7963 = const()[name = tensor("op_7963"), val = tensor([1, 1])]; + tensor v_159_pad_type_0 = const()[name = tensor("v_159_pad_type_0"), val = tensor("custom")]; + tensor v_159_pad_0 = const()[name = tensor("v_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1109897664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1111863808))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_159_cast_fp16 = conv(dilations = var_7963, groups = var_6865, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_7961, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_159_cast_fp16")]; + tensor var_7967 = const()[name = tensor("op_7967"), val = tensor([1, 20, 64, -1])]; + tensor var_7968_cast_fp16 = reshape(shape = var_7967, x = q_159_cast_fp16)[name = tensor("op_7968_cast_fp16")]; + tensor var_7969 = const()[name = tensor("op_7969"), val = tensor([1, 20, 64, -1])]; + tensor var_7970_cast_fp16 = reshape(shape = var_7969, x = k_159_cast_fp16)[name = tensor("op_7970_cast_fp16")]; + tensor var_7971 = const()[name = tensor("op_7971"), val = tensor([1, 20, 64, -1])]; + tensor var_7972_cast_fp16 = reshape(shape = var_7971, x = v_159_cast_fp16)[name = tensor("op_7972_cast_fp16")]; + tensor attn_weights_317_transpose_x_0 = const()[name = tensor("attn_weights_317_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_317_transpose_y_0 = const()[name = tensor("attn_weights_317_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_317_cast_fp16 = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_7968_cast_fp16, y = var_7970_cast_fp16)[name = tensor("attn_weights_317_cast_fp16")]; + tensor attn_weights_319_cast_fp16 = mul(x = attn_weights_317_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_319_cast_fp16")]; + tensor var_7976_cast_fp16 = softmax(axis = var_6849, x = attn_weights_319_cast_fp16)[name = tensor("op_7976_cast_fp16")]; + tensor attn_159_transpose_x_0 = const()[name = tensor("attn_159_transpose_x_0"), val = tensor(false)]; + tensor attn_159_transpose_y_0 = const()[name = tensor("attn_159_transpose_y_0"), val = tensor(true)]; + tensor attn_159_cast_fp16 = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_7972_cast_fp16, y = var_7976_cast_fp16)[name = tensor("attn_159_cast_fp16")]; + tensor var_7980 = const()[name = tensor("op_7980"), val = tensor([1, 1280, 1, -1])]; + tensor input_481_cast_fp16 = reshape(shape = var_7980, x = attn_159_cast_fp16)[name = tensor("input_481_cast_fp16")]; + tensor var_7985 = const()[name = tensor("op_7985"), val = tensor([1, 1])]; + tensor var_7987 = const()[name = tensor("op_7987"), val = tensor([1, 1])]; + tensor var_7989_pad_type_0 = const()[name = tensor("op_7989_pad_type_0"), val = tensor("custom")]; + tensor var_7989_pad_0 = const()[name = tensor("op_7989_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1111864000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113092864))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113093056)))]; + tensor var_7989_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_7987, groups = var_6865, pad = var_7989_pad_0, pad_type = var_7989_pad_type_0, strides = var_7985, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_481_cast_fp16)[name = tensor("op_7989_cast_fp16")]; + tensor inputs_239_cast_fp16 = add(x = var_7989_cast_fp16, y = inputs_237_cast_fp16)[name = tensor("inputs_239_cast_fp16")]; + tensor input_483_axes_0 = const()[name = tensor("input_483_axes_0"), val = tensor([1])]; + tensor input_483_gamma_0_to_fp16 = const()[name = tensor("input_483_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113095680)))]; + tensor input_483_beta_0_to_fp16 = const()[name = tensor("input_483_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113098304)))]; + tensor var_7999_to_fp16 = const()[name = tensor("op_7999_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = input_483_beta_0_to_fp16, epsilon = var_7999_to_fp16, gamma = input_483_gamma_0_to_fp16, x = inputs_239_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor var_8015 = const()[name = tensor("op_8015"), val = tensor([1, 1])]; + tensor var_8017 = const()[name = tensor("op_8017"), val = tensor([1, 1])]; + tensor var_8019_pad_type_0 = const()[name = tensor("op_8019_pad_type_0"), val = tensor("custom")]; + tensor var_8019_pad_0 = const()[name = tensor("op_8019_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113100928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122931392))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122931584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122939328))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8019_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8017, groups = var_6865, pad = var_8019_pad_0, pad_type = var_8019_pad_type_0, strides = var_8015, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_483_cast_fp16)[name = tensor("op_8019_cast_fp16")]; + tensor var_8020_split_sizes_0 = const()[name = tensor("op_8020_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8020_axis_0 = const()[name = tensor("op_8020_axis_0"), val = tensor(1)]; + tensor var_8020_cast_fp16_0, tensor var_8020_cast_fp16_1 = split(axis = var_8020_axis_0, split_sizes = var_8020_split_sizes_0, x = var_8019_cast_fp16)[name = tensor("op_8020_cast_fp16")]; + tensor var_8022_mode_0 = const()[name = tensor("op_8022_mode_0"), val = tensor("EXACT")]; + tensor var_8022_cast_fp16 = gelu(mode = var_8022_mode_0, x = var_8020_cast_fp16_1)[name = tensor("op_8022_cast_fp16")]; + tensor input_485_cast_fp16 = mul(x = var_8020_cast_fp16_0, y = var_8022_cast_fp16)[name = tensor("input_485_cast_fp16")]; + tensor var_8026 = const()[name = tensor("op_8026"), val = tensor([1, 1])]; + tensor var_8028 = const()[name = tensor("op_8028"), val = tensor([1, 1])]; + tensor var_8030_pad_type_0 = const()[name = tensor("op_8030_pad_type_0"), val = tensor("custom")]; + tensor var_8030_pad_0 = const()[name = tensor("op_8030_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122939520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127854784))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127854976)))]; + tensor var_8030_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_8028, groups = var_6865, pad = var_8030_pad_0, pad_type = var_8030_pad_type_0, strides = var_8026, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_485_cast_fp16)[name = tensor("op_8030_cast_fp16")]; + tensor inputs_241_cast_fp16 = add(x = var_8030_cast_fp16, y = inputs_239_cast_fp16)[name = tensor("inputs_241_cast_fp16")]; + tensor hidden_states_323_axes_0 = const()[name = tensor("hidden_states_323_axes_0"), val = tensor([1])]; + tensor hidden_states_323_gamma_0_to_fp16 = const()[name = tensor("hidden_states_323_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127857600)))]; + tensor hidden_states_323_beta_0_to_fp16 = const()[name = tensor("hidden_states_323_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127860224)))]; + tensor var_8046_to_fp16 = const()[name = tensor("op_8046_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_323_cast_fp16 = layer_norm(axes = hidden_states_323_axes_0, beta = hidden_states_323_beta_0_to_fp16, epsilon = var_8046_to_fp16, gamma = hidden_states_323_gamma_0_to_fp16, x = inputs_241_cast_fp16)[name = tensor("hidden_states_323_cast_fp16")]; + tensor var_8061 = const()[name = tensor("op_8061"), val = tensor([1, 1])]; + tensor var_8063 = const()[name = tensor("op_8063"), val = tensor([1, 1])]; + tensor q_161_pad_type_0 = const()[name = tensor("q_161_pad_type_0"), val = tensor("custom")]; + tensor q_161_pad_0 = const()[name = tensor("q_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1127862848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129091712))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_161_cast_fp16 = conv(dilations = var_8063, groups = var_6865, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_8061, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_323_cast_fp16)[name = tensor("q_161_cast_fp16")]; + tensor var_8067 = const()[name = tensor("op_8067"), val = tensor([1, 1])]; + tensor var_8069 = const()[name = tensor("op_8069"), val = tensor([1, 1])]; + tensor k_161_pad_type_0 = const()[name = tensor("k_161_pad_type_0"), val = tensor("custom")]; + tensor k_161_pad_0 = const()[name = tensor("k_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129091904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1130320768))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_161_cast_fp16 = conv(dilations = var_8069, groups = var_6865, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_8067, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_323_cast_fp16)[name = tensor("k_161_cast_fp16")]; + tensor var_8073 = const()[name = tensor("op_8073"), val = tensor([1, 1])]; + tensor var_8075 = const()[name = tensor("op_8075"), val = tensor([1, 1])]; + tensor v_161_pad_type_0 = const()[name = tensor("v_161_pad_type_0"), val = tensor("custom")]; + tensor v_161_pad_0 = const()[name = tensor("v_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1130320960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131549824))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_161_cast_fp16 = conv(dilations = var_8075, groups = var_6865, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_8073, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_323_cast_fp16)[name = tensor("v_161_cast_fp16")]; + tensor var_8079 = const()[name = tensor("op_8079"), val = tensor([1, 20, 64, -1])]; + tensor var_8080_cast_fp16 = reshape(shape = var_8079, x = q_161_cast_fp16)[name = tensor("op_8080_cast_fp16")]; + tensor var_8081 = const()[name = tensor("op_8081"), val = tensor([1, 20, 64, -1])]; + tensor var_8082_cast_fp16 = reshape(shape = var_8081, x = k_161_cast_fp16)[name = tensor("op_8082_cast_fp16")]; + tensor var_8083 = const()[name = tensor("op_8083"), val = tensor([1, 20, 64, -1])]; + tensor var_8084_cast_fp16 = reshape(shape = var_8083, x = v_161_cast_fp16)[name = tensor("op_8084_cast_fp16")]; + tensor attn_weights_321_transpose_x_0 = const()[name = tensor("attn_weights_321_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_321_transpose_y_0 = const()[name = tensor("attn_weights_321_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_8080_cast_fp16, y = var_8082_cast_fp16)[name = tensor("attn_weights_321_cast_fp16")]; + tensor attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_323_cast_fp16")]; + tensor var_8088_cast_fp16 = softmax(axis = var_6849, x = attn_weights_323_cast_fp16)[name = tensor("op_8088_cast_fp16")]; + tensor attn_161_transpose_x_0 = const()[name = tensor("attn_161_transpose_x_0"), val = tensor(false)]; + tensor attn_161_transpose_y_0 = const()[name = tensor("attn_161_transpose_y_0"), val = tensor(true)]; + tensor attn_161_cast_fp16 = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_8084_cast_fp16, y = var_8088_cast_fp16)[name = tensor("attn_161_cast_fp16")]; + tensor var_8092 = const()[name = tensor("op_8092"), val = tensor([1, 1280, 1, -1])]; + tensor input_487_cast_fp16 = reshape(shape = var_8092, x = attn_161_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor var_8097 = const()[name = tensor("op_8097"), val = tensor([1, 1])]; + tensor var_8099 = const()[name = tensor("op_8099"), val = tensor([1, 1])]; + tensor var_8101_pad_type_0 = const()[name = tensor("op_8101_pad_type_0"), val = tensor("custom")]; + tensor var_8101_pad_0 = const()[name = tensor("op_8101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131550016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132778880))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132779072)))]; + tensor var_8101_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_8099, groups = var_6865, pad = var_8101_pad_0, pad_type = var_8101_pad_type_0, strides = var_8097, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_487_cast_fp16)[name = tensor("op_8101_cast_fp16")]; + tensor inputs_243_cast_fp16 = add(x = var_8101_cast_fp16, y = inputs_241_cast_fp16)[name = tensor("inputs_243_cast_fp16")]; + tensor hidden_states_325_axes_0 = const()[name = tensor("hidden_states_325_axes_0"), val = tensor([1])]; + tensor hidden_states_325_gamma_0_to_fp16 = const()[name = tensor("hidden_states_325_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132781696)))]; + tensor hidden_states_325_beta_0_to_fp16 = const()[name = tensor("hidden_states_325_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132784320)))]; + tensor var_8111_to_fp16 = const()[name = tensor("op_8111_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_325_cast_fp16 = layer_norm(axes = hidden_states_325_axes_0, beta = hidden_states_325_beta_0_to_fp16, epsilon = var_8111_to_fp16, gamma = hidden_states_325_gamma_0_to_fp16, x = inputs_243_cast_fp16)[name = tensor("hidden_states_325_cast_fp16")]; + tensor var_8126 = const()[name = tensor("op_8126"), val = tensor([1, 1])]; + tensor var_8128 = const()[name = tensor("op_8128"), val = tensor([1, 1])]; + tensor q_163_pad_type_0 = const()[name = tensor("q_163_pad_type_0"), val = tensor("custom")]; + tensor q_163_pad_0 = const()[name = tensor("q_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132786944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1134015808))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_163_cast_fp16 = conv(dilations = var_8128, groups = var_6865, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8126, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_325_cast_fp16)[name = tensor("q_163_cast_fp16")]; + tensor var_8132 = const()[name = tensor("op_8132"), val = tensor([1, 1])]; + tensor var_8134 = const()[name = tensor("op_8134"), val = tensor([1, 1])]; + tensor k_163_pad_type_0 = const()[name = tensor("k_163_pad_type_0"), val = tensor("custom")]; + tensor k_163_pad_0 = const()[name = tensor("k_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1134016000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1135982144))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_163_cast_fp16 = conv(dilations = var_8134, groups = var_6865, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8132, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_163_cast_fp16")]; + tensor var_8138 = const()[name = tensor("op_8138"), val = tensor([1, 1])]; + tensor var_8140 = const()[name = tensor("op_8140"), val = tensor([1, 1])]; + tensor v_163_pad_type_0 = const()[name = tensor("v_163_pad_type_0"), val = tensor("custom")]; + tensor v_163_pad_0 = const()[name = tensor("v_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1135982336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137948480))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_163_cast_fp16 = conv(dilations = var_8140, groups = var_6865, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8138, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_163_cast_fp16")]; + tensor var_8144 = const()[name = tensor("op_8144"), val = tensor([1, 20, 64, -1])]; + tensor var_8145_cast_fp16 = reshape(shape = var_8144, x = q_163_cast_fp16)[name = tensor("op_8145_cast_fp16")]; + tensor var_8146 = const()[name = tensor("op_8146"), val = tensor([1, 20, 64, -1])]; + tensor var_8147_cast_fp16 = reshape(shape = var_8146, x = k_163_cast_fp16)[name = tensor("op_8147_cast_fp16")]; + tensor var_8148 = const()[name = tensor("op_8148"), val = tensor([1, 20, 64, -1])]; + tensor var_8149_cast_fp16 = reshape(shape = var_8148, x = v_163_cast_fp16)[name = tensor("op_8149_cast_fp16")]; + tensor attn_weights_325_transpose_x_0 = const()[name = tensor("attn_weights_325_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_325_transpose_y_0 = const()[name = tensor("attn_weights_325_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_325_cast_fp16 = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8145_cast_fp16, y = var_8147_cast_fp16)[name = tensor("attn_weights_325_cast_fp16")]; + tensor attn_weights_327_cast_fp16 = mul(x = attn_weights_325_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_327_cast_fp16")]; + tensor var_8153_cast_fp16 = softmax(axis = var_6849, x = attn_weights_327_cast_fp16)[name = tensor("op_8153_cast_fp16")]; + tensor attn_163_transpose_x_0 = const()[name = tensor("attn_163_transpose_x_0"), val = tensor(false)]; + tensor attn_163_transpose_y_0 = const()[name = tensor("attn_163_transpose_y_0"), val = tensor(true)]; + tensor attn_163_cast_fp16 = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8149_cast_fp16, y = var_8153_cast_fp16)[name = tensor("attn_163_cast_fp16")]; + tensor var_8157 = const()[name = tensor("op_8157"), val = tensor([1, 1280, 1, -1])]; + tensor input_489_cast_fp16 = reshape(shape = var_8157, x = attn_163_cast_fp16)[name = tensor("input_489_cast_fp16")]; + tensor var_8162 = const()[name = tensor("op_8162"), val = tensor([1, 1])]; + tensor var_8164 = const()[name = tensor("op_8164"), val = tensor([1, 1])]; + tensor var_8166_pad_type_0 = const()[name = tensor("op_8166_pad_type_0"), val = tensor("custom")]; + tensor var_8166_pad_0 = const()[name = tensor("op_8166_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1137948672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139177536))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139177728)))]; + tensor var_8166_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_8164, groups = var_6865, pad = var_8166_pad_0, pad_type = var_8166_pad_type_0, strides = var_8162, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_489_cast_fp16)[name = tensor("op_8166_cast_fp16")]; + tensor inputs_245_cast_fp16 = add(x = var_8166_cast_fp16, y = inputs_243_cast_fp16)[name = tensor("inputs_245_cast_fp16")]; + tensor input_491_axes_0 = const()[name = tensor("input_491_axes_0"), val = tensor([1])]; + tensor input_491_gamma_0_to_fp16 = const()[name = tensor("input_491_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139180352)))]; + tensor input_491_beta_0_to_fp16 = const()[name = tensor("input_491_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139182976)))]; + tensor var_8176_to_fp16 = const()[name = tensor("op_8176_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_491_cast_fp16 = layer_norm(axes = input_491_axes_0, beta = input_491_beta_0_to_fp16, epsilon = var_8176_to_fp16, gamma = input_491_gamma_0_to_fp16, x = inputs_245_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor var_8192 = const()[name = tensor("op_8192"), val = tensor([1, 1])]; + tensor var_8194 = const()[name = tensor("op_8194"), val = tensor([1, 1])]; + tensor var_8196_pad_type_0 = const()[name = tensor("op_8196_pad_type_0"), val = tensor("custom")]; + tensor var_8196_pad_0 = const()[name = tensor("op_8196_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139185600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149016064))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149016256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149024000))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8196_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8194, groups = var_6865, pad = var_8196_pad_0, pad_type = var_8196_pad_type_0, strides = var_8192, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_491_cast_fp16)[name = tensor("op_8196_cast_fp16")]; + tensor var_8197_split_sizes_0 = const()[name = tensor("op_8197_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8197_axis_0 = const()[name = tensor("op_8197_axis_0"), val = tensor(1)]; + tensor var_8197_cast_fp16_0, tensor var_8197_cast_fp16_1 = split(axis = var_8197_axis_0, split_sizes = var_8197_split_sizes_0, x = var_8196_cast_fp16)[name = tensor("op_8197_cast_fp16")]; + tensor var_8199_mode_0 = const()[name = tensor("op_8199_mode_0"), val = tensor("EXACT")]; + tensor var_8199_cast_fp16 = gelu(mode = var_8199_mode_0, x = var_8197_cast_fp16_1)[name = tensor("op_8199_cast_fp16")]; + tensor input_493_cast_fp16 = mul(x = var_8197_cast_fp16_0, y = var_8199_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor var_8203 = const()[name = tensor("op_8203"), val = tensor([1, 1])]; + tensor var_8205 = const()[name = tensor("op_8205"), val = tensor([1, 1])]; + tensor var_8207_pad_type_0 = const()[name = tensor("op_8207_pad_type_0"), val = tensor("custom")]; + tensor var_8207_pad_0 = const()[name = tensor("op_8207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149024192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153939456))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153939648)))]; + tensor var_8207_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_8205, groups = var_6865, pad = var_8207_pad_0, pad_type = var_8207_pad_type_0, strides = var_8203, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_493_cast_fp16)[name = tensor("op_8207_cast_fp16")]; + tensor inputs_247_cast_fp16 = add(x = var_8207_cast_fp16, y = inputs_245_cast_fp16)[name = tensor("inputs_247_cast_fp16")]; + tensor hidden_states_329_axes_0 = const()[name = tensor("hidden_states_329_axes_0"), val = tensor([1])]; + tensor hidden_states_329_gamma_0_to_fp16 = const()[name = tensor("hidden_states_329_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153942272)))]; + tensor hidden_states_329_beta_0_to_fp16 = const()[name = tensor("hidden_states_329_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153944896)))]; + tensor var_8223_to_fp16 = const()[name = tensor("op_8223_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_329_cast_fp16 = layer_norm(axes = hidden_states_329_axes_0, beta = hidden_states_329_beta_0_to_fp16, epsilon = var_8223_to_fp16, gamma = hidden_states_329_gamma_0_to_fp16, x = inputs_247_cast_fp16)[name = tensor("hidden_states_329_cast_fp16")]; + tensor var_8238 = const()[name = tensor("op_8238"), val = tensor([1, 1])]; + tensor var_8240 = const()[name = tensor("op_8240"), val = tensor([1, 1])]; + tensor q_165_pad_type_0 = const()[name = tensor("q_165_pad_type_0"), val = tensor("custom")]; + tensor q_165_pad_0 = const()[name = tensor("q_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153947520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1155176384))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_165_cast_fp16 = conv(dilations = var_8240, groups = var_6865, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8238, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("q_165_cast_fp16")]; + tensor var_8244 = const()[name = tensor("op_8244"), val = tensor([1, 1])]; + tensor var_8246 = const()[name = tensor("op_8246"), val = tensor([1, 1])]; + tensor k_165_pad_type_0 = const()[name = tensor("k_165_pad_type_0"), val = tensor("custom")]; + tensor k_165_pad_0 = const()[name = tensor("k_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1155176576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156405440))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_165_cast_fp16 = conv(dilations = var_8246, groups = var_6865, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8244, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("k_165_cast_fp16")]; + tensor var_8250 = const()[name = tensor("op_8250"), val = tensor([1, 1])]; + tensor var_8252 = const()[name = tensor("op_8252"), val = tensor([1, 1])]; + tensor v_165_pad_type_0 = const()[name = tensor("v_165_pad_type_0"), val = tensor("custom")]; + tensor v_165_pad_0 = const()[name = tensor("v_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156405632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157634496))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_165_cast_fp16 = conv(dilations = var_8252, groups = var_6865, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8250, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_329_cast_fp16)[name = tensor("v_165_cast_fp16")]; + tensor var_8256 = const()[name = tensor("op_8256"), val = tensor([1, 20, 64, -1])]; + tensor var_8257_cast_fp16 = reshape(shape = var_8256, x = q_165_cast_fp16)[name = tensor("op_8257_cast_fp16")]; + tensor var_8258 = const()[name = tensor("op_8258"), val = tensor([1, 20, 64, -1])]; + tensor var_8259_cast_fp16 = reshape(shape = var_8258, x = k_165_cast_fp16)[name = tensor("op_8259_cast_fp16")]; + tensor var_8260 = const()[name = tensor("op_8260"), val = tensor([1, 20, 64, -1])]; + tensor var_8261_cast_fp16 = reshape(shape = var_8260, x = v_165_cast_fp16)[name = tensor("op_8261_cast_fp16")]; + tensor attn_weights_329_transpose_x_0 = const()[name = tensor("attn_weights_329_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_329_transpose_y_0 = const()[name = tensor("attn_weights_329_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8257_cast_fp16, y = var_8259_cast_fp16)[name = tensor("attn_weights_329_cast_fp16")]; + tensor attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_331_cast_fp16")]; + tensor var_8265_cast_fp16 = softmax(axis = var_6849, x = attn_weights_331_cast_fp16)[name = tensor("op_8265_cast_fp16")]; + tensor attn_165_transpose_x_0 = const()[name = tensor("attn_165_transpose_x_0"), val = tensor(false)]; + tensor attn_165_transpose_y_0 = const()[name = tensor("attn_165_transpose_y_0"), val = tensor(true)]; + tensor attn_165_cast_fp16 = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8261_cast_fp16, y = var_8265_cast_fp16)[name = tensor("attn_165_cast_fp16")]; + tensor var_8269 = const()[name = tensor("op_8269"), val = tensor([1, 1280, 1, -1])]; + tensor input_495_cast_fp16 = reshape(shape = var_8269, x = attn_165_cast_fp16)[name = tensor("input_495_cast_fp16")]; + tensor var_8274 = const()[name = tensor("op_8274"), val = tensor([1, 1])]; + tensor var_8276 = const()[name = tensor("op_8276"), val = tensor([1, 1])]; + tensor var_8278_pad_type_0 = const()[name = tensor("op_8278_pad_type_0"), val = tensor("custom")]; + tensor var_8278_pad_0 = const()[name = tensor("op_8278_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157634688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158863552))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158863744)))]; + tensor var_8278_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_8276, groups = var_6865, pad = var_8278_pad_0, pad_type = var_8278_pad_type_0, strides = var_8274, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_495_cast_fp16)[name = tensor("op_8278_cast_fp16")]; + tensor inputs_249_cast_fp16 = add(x = var_8278_cast_fp16, y = inputs_247_cast_fp16)[name = tensor("inputs_249_cast_fp16")]; + tensor hidden_states_331_axes_0 = const()[name = tensor("hidden_states_331_axes_0"), val = tensor([1])]; + tensor hidden_states_331_gamma_0_to_fp16 = const()[name = tensor("hidden_states_331_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158866368)))]; + tensor hidden_states_331_beta_0_to_fp16 = const()[name = tensor("hidden_states_331_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158868992)))]; + tensor var_8288_to_fp16 = const()[name = tensor("op_8288_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_331_cast_fp16 = layer_norm(axes = hidden_states_331_axes_0, beta = hidden_states_331_beta_0_to_fp16, epsilon = var_8288_to_fp16, gamma = hidden_states_331_gamma_0_to_fp16, x = inputs_249_cast_fp16)[name = tensor("hidden_states_331_cast_fp16")]; + tensor var_8303 = const()[name = tensor("op_8303"), val = tensor([1, 1])]; + tensor var_8305 = const()[name = tensor("op_8305"), val = tensor([1, 1])]; + tensor q_167_pad_type_0 = const()[name = tensor("q_167_pad_type_0"), val = tensor("custom")]; + tensor q_167_pad_0 = const()[name = tensor("q_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1158871616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1160100480))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_167_cast_fp16 = conv(dilations = var_8305, groups = var_6865, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_8303, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_331_cast_fp16)[name = tensor("q_167_cast_fp16")]; + tensor var_8309 = const()[name = tensor("op_8309"), val = tensor([1, 1])]; + tensor var_8311 = const()[name = tensor("op_8311"), val = tensor([1, 1])]; + tensor k_167_pad_type_0 = const()[name = tensor("k_167_pad_type_0"), val = tensor("custom")]; + tensor k_167_pad_0 = const()[name = tensor("k_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1160100672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1162066816))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_167_cast_fp16 = conv(dilations = var_8311, groups = var_6865, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_8309, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_167_cast_fp16")]; + tensor var_8315 = const()[name = tensor("op_8315"), val = tensor([1, 1])]; + tensor var_8317 = const()[name = tensor("op_8317"), val = tensor([1, 1])]; + tensor v_167_pad_type_0 = const()[name = tensor("v_167_pad_type_0"), val = tensor("custom")]; + tensor v_167_pad_0 = const()[name = tensor("v_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1162067008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1164033152))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_167_cast_fp16 = conv(dilations = var_8317, groups = var_6865, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_8315, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_167_cast_fp16")]; + tensor var_8321 = const()[name = tensor("op_8321"), val = tensor([1, 20, 64, -1])]; + tensor var_8322_cast_fp16 = reshape(shape = var_8321, x = q_167_cast_fp16)[name = tensor("op_8322_cast_fp16")]; + tensor var_8323 = const()[name = tensor("op_8323"), val = tensor([1, 20, 64, -1])]; + tensor var_8324_cast_fp16 = reshape(shape = var_8323, x = k_167_cast_fp16)[name = tensor("op_8324_cast_fp16")]; + tensor var_8325 = const()[name = tensor("op_8325"), val = tensor([1, 20, 64, -1])]; + tensor var_8326_cast_fp16 = reshape(shape = var_8325, x = v_167_cast_fp16)[name = tensor("op_8326_cast_fp16")]; + tensor attn_weights_333_transpose_x_0 = const()[name = tensor("attn_weights_333_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_333_transpose_y_0 = const()[name = tensor("attn_weights_333_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_333_cast_fp16 = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_8322_cast_fp16, y = var_8324_cast_fp16)[name = tensor("attn_weights_333_cast_fp16")]; + tensor attn_weights_335_cast_fp16 = mul(x = attn_weights_333_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_335_cast_fp16")]; + tensor var_8330_cast_fp16 = softmax(axis = var_6849, x = attn_weights_335_cast_fp16)[name = tensor("op_8330_cast_fp16")]; + tensor attn_167_transpose_x_0 = const()[name = tensor("attn_167_transpose_x_0"), val = tensor(false)]; + tensor attn_167_transpose_y_0 = const()[name = tensor("attn_167_transpose_y_0"), val = tensor(true)]; + tensor attn_167_cast_fp16 = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_8326_cast_fp16, y = var_8330_cast_fp16)[name = tensor("attn_167_cast_fp16")]; + tensor var_8334 = const()[name = tensor("op_8334"), val = tensor([1, 1280, 1, -1])]; + tensor input_497_cast_fp16 = reshape(shape = var_8334, x = attn_167_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor var_8339 = const()[name = tensor("op_8339"), val = tensor([1, 1])]; + tensor var_8341 = const()[name = tensor("op_8341"), val = tensor([1, 1])]; + tensor var_8343_pad_type_0 = const()[name = tensor("op_8343_pad_type_0"), val = tensor("custom")]; + tensor var_8343_pad_0 = const()[name = tensor("op_8343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1164033344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165262208))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165262400)))]; + tensor var_8343_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_8341, groups = var_6865, pad = var_8343_pad_0, pad_type = var_8343_pad_type_0, strides = var_8339, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_497_cast_fp16)[name = tensor("op_8343_cast_fp16")]; + tensor inputs_251_cast_fp16 = add(x = var_8343_cast_fp16, y = inputs_249_cast_fp16)[name = tensor("inputs_251_cast_fp16")]; + tensor input_499_axes_0 = const()[name = tensor("input_499_axes_0"), val = tensor([1])]; + tensor input_499_gamma_0_to_fp16 = const()[name = tensor("input_499_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165265024)))]; + tensor input_499_beta_0_to_fp16 = const()[name = tensor("input_499_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165267648)))]; + tensor var_8353_to_fp16 = const()[name = tensor("op_8353_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_499_cast_fp16 = layer_norm(axes = input_499_axes_0, beta = input_499_beta_0_to_fp16, epsilon = var_8353_to_fp16, gamma = input_499_gamma_0_to_fp16, x = inputs_251_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor var_8369 = const()[name = tensor("op_8369"), val = tensor([1, 1])]; + tensor var_8371 = const()[name = tensor("op_8371"), val = tensor([1, 1])]; + tensor var_8373_pad_type_0 = const()[name = tensor("op_8373_pad_type_0"), val = tensor("custom")]; + tensor var_8373_pad_0 = const()[name = tensor("op_8373_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1165270272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175100736))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175100928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175108672))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8373_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8371, groups = var_6865, pad = var_8373_pad_0, pad_type = var_8373_pad_type_0, strides = var_8369, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_499_cast_fp16)[name = tensor("op_8373_cast_fp16")]; + tensor var_8374_split_sizes_0 = const()[name = tensor("op_8374_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8374_axis_0 = const()[name = tensor("op_8374_axis_0"), val = tensor(1)]; + tensor var_8374_cast_fp16_0, tensor var_8374_cast_fp16_1 = split(axis = var_8374_axis_0, split_sizes = var_8374_split_sizes_0, x = var_8373_cast_fp16)[name = tensor("op_8374_cast_fp16")]; + tensor var_8376_mode_0 = const()[name = tensor("op_8376_mode_0"), val = tensor("EXACT")]; + tensor var_8376_cast_fp16 = gelu(mode = var_8376_mode_0, x = var_8374_cast_fp16_1)[name = tensor("op_8376_cast_fp16")]; + tensor input_501_cast_fp16 = mul(x = var_8374_cast_fp16_0, y = var_8376_cast_fp16)[name = tensor("input_501_cast_fp16")]; + tensor var_8380 = const()[name = tensor("op_8380"), val = tensor([1, 1])]; + tensor var_8382 = const()[name = tensor("op_8382"), val = tensor([1, 1])]; + tensor var_8384_pad_type_0 = const()[name = tensor("op_8384_pad_type_0"), val = tensor("custom")]; + tensor var_8384_pad_0 = const()[name = tensor("op_8384_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1175108864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180024128))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180024320)))]; + tensor var_8384_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_8382, groups = var_6865, pad = var_8384_pad_0, pad_type = var_8384_pad_type_0, strides = var_8380, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_501_cast_fp16)[name = tensor("op_8384_cast_fp16")]; + tensor inputs_253_cast_fp16 = add(x = var_8384_cast_fp16, y = inputs_251_cast_fp16)[name = tensor("inputs_253_cast_fp16")]; + tensor hidden_states_335_axes_0 = const()[name = tensor("hidden_states_335_axes_0"), val = tensor([1])]; + tensor hidden_states_335_gamma_0_to_fp16 = const()[name = tensor("hidden_states_335_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180026944)))]; + tensor hidden_states_335_beta_0_to_fp16 = const()[name = tensor("hidden_states_335_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180029568)))]; + tensor var_8400_to_fp16 = const()[name = tensor("op_8400_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_335_cast_fp16 = layer_norm(axes = hidden_states_335_axes_0, beta = hidden_states_335_beta_0_to_fp16, epsilon = var_8400_to_fp16, gamma = hidden_states_335_gamma_0_to_fp16, x = inputs_253_cast_fp16)[name = tensor("hidden_states_335_cast_fp16")]; + tensor var_8415 = const()[name = tensor("op_8415"), val = tensor([1, 1])]; + tensor var_8417 = const()[name = tensor("op_8417"), val = tensor([1, 1])]; + tensor q_169_pad_type_0 = const()[name = tensor("q_169_pad_type_0"), val = tensor("custom")]; + tensor q_169_pad_0 = const()[name = tensor("q_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180032192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1181261056))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_169_cast_fp16 = conv(dilations = var_8417, groups = var_6865, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_8415, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_335_cast_fp16)[name = tensor("q_169_cast_fp16")]; + tensor var_8421 = const()[name = tensor("op_8421"), val = tensor([1, 1])]; + tensor var_8423 = const()[name = tensor("op_8423"), val = tensor([1, 1])]; + tensor k_169_pad_type_0 = const()[name = tensor("k_169_pad_type_0"), val = tensor("custom")]; + tensor k_169_pad_0 = const()[name = tensor("k_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1181261248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1182490112))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_169_cast_fp16 = conv(dilations = var_8423, groups = var_6865, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_8421, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_335_cast_fp16)[name = tensor("k_169_cast_fp16")]; + tensor var_8427 = const()[name = tensor("op_8427"), val = tensor([1, 1])]; + tensor var_8429 = const()[name = tensor("op_8429"), val = tensor([1, 1])]; + tensor v_169_pad_type_0 = const()[name = tensor("v_169_pad_type_0"), val = tensor("custom")]; + tensor v_169_pad_0 = const()[name = tensor("v_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1182490304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183719168))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_169_cast_fp16 = conv(dilations = var_8429, groups = var_6865, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_8427, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_335_cast_fp16)[name = tensor("v_169_cast_fp16")]; + tensor var_8433 = const()[name = tensor("op_8433"), val = tensor([1, 20, 64, -1])]; + tensor var_8434_cast_fp16 = reshape(shape = var_8433, x = q_169_cast_fp16)[name = tensor("op_8434_cast_fp16")]; + tensor var_8435 = const()[name = tensor("op_8435"), val = tensor([1, 20, 64, -1])]; + tensor var_8436_cast_fp16 = reshape(shape = var_8435, x = k_169_cast_fp16)[name = tensor("op_8436_cast_fp16")]; + tensor var_8437 = const()[name = tensor("op_8437"), val = tensor([1, 20, 64, -1])]; + tensor var_8438_cast_fp16 = reshape(shape = var_8437, x = v_169_cast_fp16)[name = tensor("op_8438_cast_fp16")]; + tensor attn_weights_337_transpose_x_0 = const()[name = tensor("attn_weights_337_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_337_transpose_y_0 = const()[name = tensor("attn_weights_337_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_8434_cast_fp16, y = var_8436_cast_fp16)[name = tensor("attn_weights_337_cast_fp16")]; + tensor attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_339_cast_fp16")]; + tensor var_8442_cast_fp16 = softmax(axis = var_6849, x = attn_weights_339_cast_fp16)[name = tensor("op_8442_cast_fp16")]; + tensor attn_169_transpose_x_0 = const()[name = tensor("attn_169_transpose_x_0"), val = tensor(false)]; + tensor attn_169_transpose_y_0 = const()[name = tensor("attn_169_transpose_y_0"), val = tensor(true)]; + tensor attn_169_cast_fp16 = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_8438_cast_fp16, y = var_8442_cast_fp16)[name = tensor("attn_169_cast_fp16")]; + tensor var_8446 = const()[name = tensor("op_8446"), val = tensor([1, 1280, 1, -1])]; + tensor input_503_cast_fp16 = reshape(shape = var_8446, x = attn_169_cast_fp16)[name = tensor("input_503_cast_fp16")]; + tensor var_8451 = const()[name = tensor("op_8451"), val = tensor([1, 1])]; + tensor var_8453 = const()[name = tensor("op_8453"), val = tensor([1, 1])]; + tensor var_8455_pad_type_0 = const()[name = tensor("op_8455_pad_type_0"), val = tensor("custom")]; + tensor var_8455_pad_0 = const()[name = tensor("op_8455_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183719360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184948224))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184948416)))]; + tensor var_8455_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_8453, groups = var_6865, pad = var_8455_pad_0, pad_type = var_8455_pad_type_0, strides = var_8451, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_503_cast_fp16)[name = tensor("op_8455_cast_fp16")]; + tensor inputs_255_cast_fp16 = add(x = var_8455_cast_fp16, y = inputs_253_cast_fp16)[name = tensor("inputs_255_cast_fp16")]; + tensor hidden_states_337_axes_0 = const()[name = tensor("hidden_states_337_axes_0"), val = tensor([1])]; + tensor hidden_states_337_gamma_0_to_fp16 = const()[name = tensor("hidden_states_337_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184951040)))]; + tensor hidden_states_337_beta_0_to_fp16 = const()[name = tensor("hidden_states_337_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184953664)))]; + tensor var_8465_to_fp16 = const()[name = tensor("op_8465_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_337_cast_fp16 = layer_norm(axes = hidden_states_337_axes_0, beta = hidden_states_337_beta_0_to_fp16, epsilon = var_8465_to_fp16, gamma = hidden_states_337_gamma_0_to_fp16, x = inputs_255_cast_fp16)[name = tensor("hidden_states_337_cast_fp16")]; + tensor var_8480 = const()[name = tensor("op_8480"), val = tensor([1, 1])]; + tensor var_8482 = const()[name = tensor("op_8482"), val = tensor([1, 1])]; + tensor q_171_pad_type_0 = const()[name = tensor("q_171_pad_type_0"), val = tensor("custom")]; + tensor q_171_pad_0 = const()[name = tensor("q_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1184956288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1186185152))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_171_cast_fp16 = conv(dilations = var_8482, groups = var_6865, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_8480, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_337_cast_fp16)[name = tensor("q_171_cast_fp16")]; + tensor var_8486 = const()[name = tensor("op_8486"), val = tensor([1, 1])]; + tensor var_8488 = const()[name = tensor("op_8488"), val = tensor([1, 1])]; + tensor k_171_pad_type_0 = const()[name = tensor("k_171_pad_type_0"), val = tensor("custom")]; + tensor k_171_pad_0 = const()[name = tensor("k_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1186185344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1188151488))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_171_cast_fp16 = conv(dilations = var_8488, groups = var_6865, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_8486, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_171_cast_fp16")]; + tensor var_8492 = const()[name = tensor("op_8492"), val = tensor([1, 1])]; + tensor var_8494 = const()[name = tensor("op_8494"), val = tensor([1, 1])]; + tensor v_171_pad_type_0 = const()[name = tensor("v_171_pad_type_0"), val = tensor("custom")]; + tensor v_171_pad_0 = const()[name = tensor("v_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1188151680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1190117824))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_171_cast_fp16 = conv(dilations = var_8494, groups = var_6865, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_8492, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_171_cast_fp16")]; + tensor var_8498 = const()[name = tensor("op_8498"), val = tensor([1, 20, 64, -1])]; + tensor var_8499_cast_fp16 = reshape(shape = var_8498, x = q_171_cast_fp16)[name = tensor("op_8499_cast_fp16")]; + tensor var_8500 = const()[name = tensor("op_8500"), val = tensor([1, 20, 64, -1])]; + tensor var_8501_cast_fp16 = reshape(shape = var_8500, x = k_171_cast_fp16)[name = tensor("op_8501_cast_fp16")]; + tensor var_8502 = const()[name = tensor("op_8502"), val = tensor([1, 20, 64, -1])]; + tensor var_8503_cast_fp16 = reshape(shape = var_8502, x = v_171_cast_fp16)[name = tensor("op_8503_cast_fp16")]; + tensor attn_weights_341_transpose_x_0 = const()[name = tensor("attn_weights_341_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_341_transpose_y_0 = const()[name = tensor("attn_weights_341_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_341_cast_fp16 = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_8499_cast_fp16, y = var_8501_cast_fp16)[name = tensor("attn_weights_341_cast_fp16")]; + tensor attn_weights_343_cast_fp16 = mul(x = attn_weights_341_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_343_cast_fp16")]; + tensor var_8507_cast_fp16 = softmax(axis = var_6849, x = attn_weights_343_cast_fp16)[name = tensor("op_8507_cast_fp16")]; + tensor attn_171_transpose_x_0 = const()[name = tensor("attn_171_transpose_x_0"), val = tensor(false)]; + tensor attn_171_transpose_y_0 = const()[name = tensor("attn_171_transpose_y_0"), val = tensor(true)]; + tensor attn_171_cast_fp16 = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_8503_cast_fp16, y = var_8507_cast_fp16)[name = tensor("attn_171_cast_fp16")]; + tensor var_8511 = const()[name = tensor("op_8511"), val = tensor([1, 1280, 1, -1])]; + tensor input_505_cast_fp16 = reshape(shape = var_8511, x = attn_171_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor var_8516 = const()[name = tensor("op_8516"), val = tensor([1, 1])]; + tensor var_8518 = const()[name = tensor("op_8518"), val = tensor([1, 1])]; + tensor var_8520_pad_type_0 = const()[name = tensor("op_8520_pad_type_0"), val = tensor("custom")]; + tensor var_8520_pad_0 = const()[name = tensor("op_8520_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1190118016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191346880))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191347072)))]; + tensor var_8520_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_8518, groups = var_6865, pad = var_8520_pad_0, pad_type = var_8520_pad_type_0, strides = var_8516, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_505_cast_fp16)[name = tensor("op_8520_cast_fp16")]; + tensor inputs_257_cast_fp16 = add(x = var_8520_cast_fp16, y = inputs_255_cast_fp16)[name = tensor("inputs_257_cast_fp16")]; + tensor input_507_axes_0 = const()[name = tensor("input_507_axes_0"), val = tensor([1])]; + tensor input_507_gamma_0_to_fp16 = const()[name = tensor("input_507_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191349696)))]; + tensor input_507_beta_0_to_fp16 = const()[name = tensor("input_507_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191352320)))]; + tensor var_8530_to_fp16 = const()[name = tensor("op_8530_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_507_cast_fp16 = layer_norm(axes = input_507_axes_0, beta = input_507_beta_0_to_fp16, epsilon = var_8530_to_fp16, gamma = input_507_gamma_0_to_fp16, x = inputs_257_cast_fp16)[name = tensor("input_507_cast_fp16")]; + tensor var_8546 = const()[name = tensor("op_8546"), val = tensor([1, 1])]; + tensor var_8548 = const()[name = tensor("op_8548"), val = tensor([1, 1])]; + tensor var_8550_pad_type_0 = const()[name = tensor("op_8550_pad_type_0"), val = tensor("custom")]; + tensor var_8550_pad_0 = const()[name = tensor("op_8550_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191354944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201185408))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201185600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201193344))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8550_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8548, groups = var_6865, pad = var_8550_pad_0, pad_type = var_8550_pad_type_0, strides = var_8546, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = tensor("op_8550_cast_fp16")]; + tensor var_8551_split_sizes_0 = const()[name = tensor("op_8551_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8551_axis_0 = const()[name = tensor("op_8551_axis_0"), val = tensor(1)]; + tensor var_8551_cast_fp16_0, tensor var_8551_cast_fp16_1 = split(axis = var_8551_axis_0, split_sizes = var_8551_split_sizes_0, x = var_8550_cast_fp16)[name = tensor("op_8551_cast_fp16")]; + tensor var_8553_mode_0 = const()[name = tensor("op_8553_mode_0"), val = tensor("EXACT")]; + tensor var_8553_cast_fp16 = gelu(mode = var_8553_mode_0, x = var_8551_cast_fp16_1)[name = tensor("op_8553_cast_fp16")]; + tensor input_509_cast_fp16 = mul(x = var_8551_cast_fp16_0, y = var_8553_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor var_8557 = const()[name = tensor("op_8557"), val = tensor([1, 1])]; + tensor var_8559 = const()[name = tensor("op_8559"), val = tensor([1, 1])]; + tensor var_8561_pad_type_0 = const()[name = tensor("op_8561_pad_type_0"), val = tensor("custom")]; + tensor var_8561_pad_0 = const()[name = tensor("op_8561_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1201193536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206108800))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206108992)))]; + tensor var_8561_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_8559, groups = var_6865, pad = var_8561_pad_0, pad_type = var_8561_pad_type_0, strides = var_8557, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_509_cast_fp16)[name = tensor("op_8561_cast_fp16")]; + tensor inputs_259_cast_fp16 = add(x = var_8561_cast_fp16, y = inputs_257_cast_fp16)[name = tensor("inputs_259_cast_fp16")]; + tensor hidden_states_341_axes_0 = const()[name = tensor("hidden_states_341_axes_0"), val = tensor([1])]; + tensor hidden_states_341_gamma_0_to_fp16 = const()[name = tensor("hidden_states_341_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206111616)))]; + tensor hidden_states_341_beta_0_to_fp16 = const()[name = tensor("hidden_states_341_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206114240)))]; + tensor var_8577_to_fp16 = const()[name = tensor("op_8577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_341_cast_fp16 = layer_norm(axes = hidden_states_341_axes_0, beta = hidden_states_341_beta_0_to_fp16, epsilon = var_8577_to_fp16, gamma = hidden_states_341_gamma_0_to_fp16, x = inputs_259_cast_fp16)[name = tensor("hidden_states_341_cast_fp16")]; + tensor var_8592 = const()[name = tensor("op_8592"), val = tensor([1, 1])]; + tensor var_8594 = const()[name = tensor("op_8594"), val = tensor([1, 1])]; + tensor q_173_pad_type_0 = const()[name = tensor("q_173_pad_type_0"), val = tensor("custom")]; + tensor q_173_pad_0 = const()[name = tensor("q_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206116864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1207345728))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_173_cast_fp16 = conv(dilations = var_8594, groups = var_6865, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_8592, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_341_cast_fp16)[name = tensor("q_173_cast_fp16")]; + tensor var_8598 = const()[name = tensor("op_8598"), val = tensor([1, 1])]; + tensor var_8600 = const()[name = tensor("op_8600"), val = tensor([1, 1])]; + tensor k_173_pad_type_0 = const()[name = tensor("k_173_pad_type_0"), val = tensor("custom")]; + tensor k_173_pad_0 = const()[name = tensor("k_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1207345920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208574784))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_173_cast_fp16 = conv(dilations = var_8600, groups = var_6865, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_8598, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_341_cast_fp16)[name = tensor("k_173_cast_fp16")]; + tensor var_8604 = const()[name = tensor("op_8604"), val = tensor([1, 1])]; + tensor var_8606 = const()[name = tensor("op_8606"), val = tensor([1, 1])]; + tensor v_173_pad_type_0 = const()[name = tensor("v_173_pad_type_0"), val = tensor("custom")]; + tensor v_173_pad_0 = const()[name = tensor("v_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1208574976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1209803840))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_173_cast_fp16 = conv(dilations = var_8606, groups = var_6865, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_8604, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_341_cast_fp16)[name = tensor("v_173_cast_fp16")]; + tensor var_8610 = const()[name = tensor("op_8610"), val = tensor([1, 20, 64, -1])]; + tensor var_8611_cast_fp16 = reshape(shape = var_8610, x = q_173_cast_fp16)[name = tensor("op_8611_cast_fp16")]; + tensor var_8612 = const()[name = tensor("op_8612"), val = tensor([1, 20, 64, -1])]; + tensor var_8613_cast_fp16 = reshape(shape = var_8612, x = k_173_cast_fp16)[name = tensor("op_8613_cast_fp16")]; + tensor var_8614 = const()[name = tensor("op_8614"), val = tensor([1, 20, 64, -1])]; + tensor var_8615_cast_fp16 = reshape(shape = var_8614, x = v_173_cast_fp16)[name = tensor("op_8615_cast_fp16")]; + tensor attn_weights_345_transpose_x_0 = const()[name = tensor("attn_weights_345_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_345_transpose_y_0 = const()[name = tensor("attn_weights_345_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_8611_cast_fp16, y = var_8613_cast_fp16)[name = tensor("attn_weights_345_cast_fp16")]; + tensor attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_347_cast_fp16")]; + tensor var_8619_cast_fp16 = softmax(axis = var_6849, x = attn_weights_347_cast_fp16)[name = tensor("op_8619_cast_fp16")]; + tensor attn_173_transpose_x_0 = const()[name = tensor("attn_173_transpose_x_0"), val = tensor(false)]; + tensor attn_173_transpose_y_0 = const()[name = tensor("attn_173_transpose_y_0"), val = tensor(true)]; + tensor attn_173_cast_fp16 = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_8615_cast_fp16, y = var_8619_cast_fp16)[name = tensor("attn_173_cast_fp16")]; + tensor var_8623 = const()[name = tensor("op_8623"), val = tensor([1, 1280, 1, -1])]; + tensor input_511_cast_fp16 = reshape(shape = var_8623, x = attn_173_cast_fp16)[name = tensor("input_511_cast_fp16")]; + tensor var_8628 = const()[name = tensor("op_8628"), val = tensor([1, 1])]; + tensor var_8630 = const()[name = tensor("op_8630"), val = tensor([1, 1])]; + tensor var_8632_pad_type_0 = const()[name = tensor("op_8632_pad_type_0"), val = tensor("custom")]; + tensor var_8632_pad_0 = const()[name = tensor("op_8632_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1209804032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211032896))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211033088)))]; + tensor var_8632_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_8630, groups = var_6865, pad = var_8632_pad_0, pad_type = var_8632_pad_type_0, strides = var_8628, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = tensor("op_8632_cast_fp16")]; + tensor inputs_261_cast_fp16 = add(x = var_8632_cast_fp16, y = inputs_259_cast_fp16)[name = tensor("inputs_261_cast_fp16")]; + tensor hidden_states_343_axes_0 = const()[name = tensor("hidden_states_343_axes_0"), val = tensor([1])]; + tensor hidden_states_343_gamma_0_to_fp16 = const()[name = tensor("hidden_states_343_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211035712)))]; + tensor hidden_states_343_beta_0_to_fp16 = const()[name = tensor("hidden_states_343_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211038336)))]; + tensor var_8642_to_fp16 = const()[name = tensor("op_8642_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_343_cast_fp16 = layer_norm(axes = hidden_states_343_axes_0, beta = hidden_states_343_beta_0_to_fp16, epsilon = var_8642_to_fp16, gamma = hidden_states_343_gamma_0_to_fp16, x = inputs_261_cast_fp16)[name = tensor("hidden_states_343_cast_fp16")]; + tensor var_8657 = const()[name = tensor("op_8657"), val = tensor([1, 1])]; + tensor var_8659 = const()[name = tensor("op_8659"), val = tensor([1, 1])]; + tensor q_175_pad_type_0 = const()[name = tensor("q_175_pad_type_0"), val = tensor("custom")]; + tensor q_175_pad_0 = const()[name = tensor("q_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211040960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1212269824))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_175_cast_fp16 = conv(dilations = var_8659, groups = var_6865, pad = q_175_pad_0, pad_type = q_175_pad_type_0, strides = var_8657, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_343_cast_fp16)[name = tensor("q_175_cast_fp16")]; + tensor var_8663 = const()[name = tensor("op_8663"), val = tensor([1, 1])]; + tensor var_8665 = const()[name = tensor("op_8665"), val = tensor([1, 1])]; + tensor k_175_pad_type_0 = const()[name = tensor("k_175_pad_type_0"), val = tensor("custom")]; + tensor k_175_pad_0 = const()[name = tensor("k_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1212270016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1214236160))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_175_cast_fp16 = conv(dilations = var_8665, groups = var_6865, pad = k_175_pad_0, pad_type = k_175_pad_type_0, strides = var_8663, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_175_cast_fp16")]; + tensor var_8669 = const()[name = tensor("op_8669"), val = tensor([1, 1])]; + tensor var_8671 = const()[name = tensor("op_8671"), val = tensor([1, 1])]; + tensor v_175_pad_type_0 = const()[name = tensor("v_175_pad_type_0"), val = tensor("custom")]; + tensor v_175_pad_0 = const()[name = tensor("v_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1214236352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1216202496))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_175_cast_fp16 = conv(dilations = var_8671, groups = var_6865, pad = v_175_pad_0, pad_type = v_175_pad_type_0, strides = var_8669, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_175_cast_fp16")]; + tensor var_8675 = const()[name = tensor("op_8675"), val = tensor([1, 20, 64, -1])]; + tensor var_8676_cast_fp16 = reshape(shape = var_8675, x = q_175_cast_fp16)[name = tensor("op_8676_cast_fp16")]; + tensor var_8677 = const()[name = tensor("op_8677"), val = tensor([1, 20, 64, -1])]; + tensor var_8678_cast_fp16 = reshape(shape = var_8677, x = k_175_cast_fp16)[name = tensor("op_8678_cast_fp16")]; + tensor var_8679 = const()[name = tensor("op_8679"), val = tensor([1, 20, 64, -1])]; + tensor var_8680_cast_fp16 = reshape(shape = var_8679, x = v_175_cast_fp16)[name = tensor("op_8680_cast_fp16")]; + tensor attn_weights_349_transpose_x_0 = const()[name = tensor("attn_weights_349_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_349_transpose_y_0 = const()[name = tensor("attn_weights_349_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_349_cast_fp16 = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_8676_cast_fp16, y = var_8678_cast_fp16)[name = tensor("attn_weights_349_cast_fp16")]; + tensor attn_weights_351_cast_fp16 = mul(x = attn_weights_349_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_351_cast_fp16")]; + tensor var_8684_cast_fp16 = softmax(axis = var_6849, x = attn_weights_351_cast_fp16)[name = tensor("op_8684_cast_fp16")]; + tensor attn_175_transpose_x_0 = const()[name = tensor("attn_175_transpose_x_0"), val = tensor(false)]; + tensor attn_175_transpose_y_0 = const()[name = tensor("attn_175_transpose_y_0"), val = tensor(true)]; + tensor attn_175_cast_fp16 = matmul(transpose_x = attn_175_transpose_x_0, transpose_y = attn_175_transpose_y_0, x = var_8680_cast_fp16, y = var_8684_cast_fp16)[name = tensor("attn_175_cast_fp16")]; + tensor var_8688 = const()[name = tensor("op_8688"), val = tensor([1, 1280, 1, -1])]; + tensor input_513_cast_fp16 = reshape(shape = var_8688, x = attn_175_cast_fp16)[name = tensor("input_513_cast_fp16")]; + tensor var_8693 = const()[name = tensor("op_8693"), val = tensor([1, 1])]; + tensor var_8695 = const()[name = tensor("op_8695"), val = tensor([1, 1])]; + tensor var_8697_pad_type_0 = const()[name = tensor("op_8697_pad_type_0"), val = tensor("custom")]; + tensor var_8697_pad_0 = const()[name = tensor("op_8697_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1216202688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217431552))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217431744)))]; + tensor var_8697_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_8695, groups = var_6865, pad = var_8697_pad_0, pad_type = var_8697_pad_type_0, strides = var_8693, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_513_cast_fp16)[name = tensor("op_8697_cast_fp16")]; + tensor inputs_263_cast_fp16 = add(x = var_8697_cast_fp16, y = inputs_261_cast_fp16)[name = tensor("inputs_263_cast_fp16")]; + tensor input_515_axes_0 = const()[name = tensor("input_515_axes_0"), val = tensor([1])]; + tensor input_515_gamma_0_to_fp16 = const()[name = tensor("input_515_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217434368)))]; + tensor input_515_beta_0_to_fp16 = const()[name = tensor("input_515_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217436992)))]; + tensor var_8707_to_fp16 = const()[name = tensor("op_8707_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_515_cast_fp16 = layer_norm(axes = input_515_axes_0, beta = input_515_beta_0_to_fp16, epsilon = var_8707_to_fp16, gamma = input_515_gamma_0_to_fp16, x = inputs_263_cast_fp16)[name = tensor("input_515_cast_fp16")]; + tensor var_8723 = const()[name = tensor("op_8723"), val = tensor([1, 1])]; + tensor var_8725 = const()[name = tensor("op_8725"), val = tensor([1, 1])]; + tensor var_8727_pad_type_0 = const()[name = tensor("op_8727_pad_type_0"), val = tensor("custom")]; + tensor var_8727_pad_0 = const()[name = tensor("op_8727_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1217439616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227270080))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227270272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227278016))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_8727_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_8725, groups = var_6865, pad = var_8727_pad_0, pad_type = var_8727_pad_type_0, strides = var_8723, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_515_cast_fp16)[name = tensor("op_8727_cast_fp16")]; + tensor var_8728_split_sizes_0 = const()[name = tensor("op_8728_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8728_axis_0 = const()[name = tensor("op_8728_axis_0"), val = tensor(1)]; + tensor var_8728_cast_fp16_0, tensor var_8728_cast_fp16_1 = split(axis = var_8728_axis_0, split_sizes = var_8728_split_sizes_0, x = var_8727_cast_fp16)[name = tensor("op_8728_cast_fp16")]; + tensor var_8730_mode_0 = const()[name = tensor("op_8730_mode_0"), val = tensor("EXACT")]; + tensor var_8730_cast_fp16 = gelu(mode = var_8730_mode_0, x = var_8728_cast_fp16_1)[name = tensor("op_8730_cast_fp16")]; + tensor input_517_cast_fp16 = mul(x = var_8728_cast_fp16_0, y = var_8730_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor var_8734 = const()[name = tensor("op_8734"), val = tensor([1, 1])]; + tensor var_8736 = const()[name = tensor("op_8736"), val = tensor([1, 1])]; + tensor var_8738_pad_type_0 = const()[name = tensor("op_8738_pad_type_0"), val = tensor("custom")]; + tensor var_8738_pad_0 = const()[name = tensor("op_8738_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227278208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1232193472))), name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1232193664)))]; + tensor var_8738_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_8736, groups = var_6865, pad = var_8738_pad_0, pad_type = var_8738_pad_type_0, strides = var_8734, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_517_cast_fp16)[name = tensor("op_8738_cast_fp16")]; + tensor hidden_states_347_cast_fp16 = add(x = var_8738_cast_fp16, y = inputs_263_cast_fp16)[name = tensor("hidden_states_347_cast_fp16")]; + tensor var_8740 = const()[name = tensor("op_8740"), val = tensor([1, 1280, 32, 32])]; + tensor input_519_cast_fp16 = reshape(shape = var_8740, x = hidden_states_347_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor var_8744 = const()[name = tensor("op_8744"), val = tensor([1, 1])]; + tensor var_8746 = const()[name = tensor("op_8746"), val = tensor([1, 1])]; + tensor hidden_states_349_pad_type_0 = const()[name = tensor("hidden_states_349_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_349_pad_0 = const()[name = tensor("hidden_states_349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1232196288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233425152))), name = tensor("up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233425344)))]; + tensor hidden_states_349_cast_fp16 = conv(bias = up_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_8746, groups = var_6865, pad = hidden_states_349_pad_0, pad_type = hidden_states_349_pad_type_0, strides = var_8744, weight = up_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_519_cast_fp16)[name = tensor("hidden_states_349_cast_fp16")]; + tensor hidden_states_351_cast_fp16 = add(x = hidden_states_349_cast_fp16, y = hidden_states_283_cast_fp16)[name = tensor("hidden_states_351_cast_fp16")]; + tensor input_521_interleave_0 = const()[name = tensor("input_521_interleave_0"), val = tensor(false)]; + tensor input_521_cast_fp16 = concat(axis = var_6865, interleave = input_521_interleave_0, values = (hidden_states_351_cast_fp16, input_213_cast_fp16))[name = tensor("input_521_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 80, 32, 32])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_521_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 2560, 32, 32])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233427968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233429952))), name = tensor("add_49_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_49_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233430144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233432128))), name = tensor("add_49_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16_palettized, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16_palettized, mean = add_43_mean_0_to_fp16_palettized, variance = add_43_variance_0_to_fp16_palettized, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor input_525_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor("input_525_cast_fp16")]; + tensor var_8764 = const()[name = tensor("op_8764"), val = tensor([1, 1])]; + tensor var_8766 = const()[name = tensor("op_8766"), val = tensor([1, 1])]; + tensor hidden_states_353_pad_type_0 = const()[name = tensor("hidden_states_353_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_353_pad_0 = const()[name = tensor("hidden_states_353_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1233432320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1255550784))), name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1255550976)))]; + tensor hidden_states_353_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_8766, groups = var_6865, pad = hidden_states_353_pad_0, pad_type = hidden_states_353_pad_type_0, strides = var_8764, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_525_cast_fp16)[name = tensor("hidden_states_353_cast_fp16")]; + tensor var_8772 = const()[name = tensor("op_8772"), val = tensor([1, 1])]; + tensor var_8774 = const()[name = tensor("op_8774"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1255553600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256782464))), name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256782656)))]; + tensor temb_19_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_8774, groups = var_6865, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_8772, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_19_cast_fp16")]; + tensor input_529_cast_fp16 = add(x = hidden_states_353_cast_fp16, y = temb_19_cast_fp16)[name = tensor("input_529_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_529_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256785280)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256787904)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor input_533_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("input_533_cast_fp16")]; + tensor var_8784 = const()[name = tensor("op_8784"), val = tensor([1, 1])]; + tensor var_8786 = const()[name = tensor("op_8786"), val = tensor([1, 1])]; + tensor hidden_states_355_pad_type_0 = const()[name = tensor("hidden_states_355_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_355_pad_0 = const()[name = tensor("hidden_states_355_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256790528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1267849792))), name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1267849984)))]; + tensor hidden_states_355_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_8786, groups = var_6865, pad = hidden_states_355_pad_0, pad_type = hidden_states_355_pad_type_0, strides = var_8784, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_533_cast_fp16)[name = tensor("hidden_states_355_cast_fp16")]; + tensor var_8791 = const()[name = tensor("op_8791"), val = tensor([1, 1])]; + tensor var_8793 = const()[name = tensor("op_8793"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1267852608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270310272))), name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270310464)))]; + tensor x_7_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_8793, groups = var_6865, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_8791, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_521_cast_fp16)[name = tensor("x_7_cast_fp16")]; + tensor hidden_states_357_cast_fp16 = add(x = x_7_cast_fp16, y = hidden_states_355_cast_fp16)[name = tensor("hidden_states_357_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = hidden_states_357_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270313088)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270315712)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor var_8831 = const()[name = tensor("op_8831"), val = tensor([1, 1])]; + tensor var_8833 = const()[name = tensor("op_8833"), val = tensor([1, 1])]; + tensor hidden_states_359_pad_type_0 = const()[name = tensor("hidden_states_359_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_359_pad_0 = const()[name = tensor("hidden_states_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270318336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271547200))), name = tensor("up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271547392)))]; + tensor hidden_states_359_cast_fp16 = conv(bias = up_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_8833, groups = var_6865, pad = hidden_states_359_pad_0, pad_type = hidden_states_359_pad_type_0, strides = var_8831, weight = up_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_53_cast_fp16)[name = tensor("hidden_states_359_cast_fp16")]; + tensor var_8838 = const()[name = tensor("op_8838"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_265_cast_fp16 = reshape(shape = var_8838, x = hidden_states_359_cast_fp16)[name = tensor("inputs_265_cast_fp16")]; + tensor hidden_states_361_axes_0 = const()[name = tensor("hidden_states_361_axes_0"), val = tensor([1])]; + tensor hidden_states_361_gamma_0_to_fp16 = const()[name = tensor("hidden_states_361_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271550016)))]; + tensor hidden_states_361_beta_0_to_fp16 = const()[name = tensor("hidden_states_361_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271552640)))]; + tensor var_8854_to_fp16 = const()[name = tensor("op_8854_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_361_cast_fp16 = layer_norm(axes = hidden_states_361_axes_0, beta = hidden_states_361_beta_0_to_fp16, epsilon = var_8854_to_fp16, gamma = hidden_states_361_gamma_0_to_fp16, x = inputs_265_cast_fp16)[name = tensor("hidden_states_361_cast_fp16")]; + tensor var_8869 = const()[name = tensor("op_8869"), val = tensor([1, 1])]; + tensor var_8871 = const()[name = tensor("op_8871"), val = tensor([1, 1])]; + tensor q_177_pad_type_0 = const()[name = tensor("q_177_pad_type_0"), val = tensor("custom")]; + tensor q_177_pad_0 = const()[name = tensor("q_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271555264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1272784128))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_177_cast_fp16 = conv(dilations = var_8871, groups = var_6865, pad = q_177_pad_0, pad_type = q_177_pad_type_0, strides = var_8869, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_361_cast_fp16)[name = tensor("q_177_cast_fp16")]; + tensor var_8875 = const()[name = tensor("op_8875"), val = tensor([1, 1])]; + tensor var_8877 = const()[name = tensor("op_8877"), val = tensor([1, 1])]; + tensor k_177_pad_type_0 = const()[name = tensor("k_177_pad_type_0"), val = tensor("custom")]; + tensor k_177_pad_0 = const()[name = tensor("k_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1272784320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1274013184))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_177_cast_fp16 = conv(dilations = var_8877, groups = var_6865, pad = k_177_pad_0, pad_type = k_177_pad_type_0, strides = var_8875, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_361_cast_fp16)[name = tensor("k_177_cast_fp16")]; + tensor var_8881 = const()[name = tensor("op_8881"), val = tensor([1, 1])]; + tensor var_8883 = const()[name = tensor("op_8883"), val = tensor([1, 1])]; + tensor v_177_pad_type_0 = const()[name = tensor("v_177_pad_type_0"), val = tensor("custom")]; + tensor v_177_pad_0 = const()[name = tensor("v_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1274013376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1275242240))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_177_cast_fp16 = conv(dilations = var_8883, groups = var_6865, pad = v_177_pad_0, pad_type = v_177_pad_type_0, strides = var_8881, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_361_cast_fp16)[name = tensor("v_177_cast_fp16")]; + tensor var_8887 = const()[name = tensor("op_8887"), val = tensor([1, 20, 64, -1])]; + tensor var_8888_cast_fp16 = reshape(shape = var_8887, x = q_177_cast_fp16)[name = tensor("op_8888_cast_fp16")]; + tensor var_8889 = const()[name = tensor("op_8889"), val = tensor([1, 20, 64, -1])]; + tensor var_8890_cast_fp16 = reshape(shape = var_8889, x = k_177_cast_fp16)[name = tensor("op_8890_cast_fp16")]; + tensor var_8891 = const()[name = tensor("op_8891"), val = tensor([1, 20, 64, -1])]; + tensor var_8892_cast_fp16 = reshape(shape = var_8891, x = v_177_cast_fp16)[name = tensor("op_8892_cast_fp16")]; + tensor attn_weights_353_transpose_x_0 = const()[name = tensor("attn_weights_353_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_353_transpose_y_0 = const()[name = tensor("attn_weights_353_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8888_cast_fp16, y = var_8890_cast_fp16)[name = tensor("attn_weights_353_cast_fp16")]; + tensor attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_355_cast_fp16")]; + tensor var_8896_cast_fp16 = softmax(axis = var_6849, x = attn_weights_355_cast_fp16)[name = tensor("op_8896_cast_fp16")]; + tensor attn_177_transpose_x_0 = const()[name = tensor("attn_177_transpose_x_0"), val = tensor(false)]; + tensor attn_177_transpose_y_0 = const()[name = tensor("attn_177_transpose_y_0"), val = tensor(true)]; + tensor attn_177_cast_fp16 = matmul(transpose_x = attn_177_transpose_x_0, transpose_y = attn_177_transpose_y_0, x = var_8892_cast_fp16, y = var_8896_cast_fp16)[name = tensor("attn_177_cast_fp16")]; + tensor var_8900 = const()[name = tensor("op_8900"), val = tensor([1, 1280, 1, -1])]; + tensor input_537_cast_fp16 = reshape(shape = var_8900, x = attn_177_cast_fp16)[name = tensor("input_537_cast_fp16")]; + tensor var_8905 = const()[name = tensor("op_8905"), val = tensor([1, 1])]; + tensor var_8907 = const()[name = tensor("op_8907"), val = tensor([1, 1])]; + tensor var_8909_pad_type_0 = const()[name = tensor("op_8909_pad_type_0"), val = tensor("custom")]; + tensor var_8909_pad_0 = const()[name = tensor("op_8909_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1275242432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276471296))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276471488)))]; + tensor var_8909_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8907, groups = var_6865, pad = var_8909_pad_0, pad_type = var_8909_pad_type_0, strides = var_8905, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_537_cast_fp16)[name = tensor("op_8909_cast_fp16")]; + tensor inputs_267_cast_fp16 = add(x = var_8909_cast_fp16, y = inputs_265_cast_fp16)[name = tensor("inputs_267_cast_fp16")]; + tensor hidden_states_363_axes_0 = const()[name = tensor("hidden_states_363_axes_0"), val = tensor([1])]; + tensor hidden_states_363_gamma_0_to_fp16 = const()[name = tensor("hidden_states_363_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276474112)))]; + tensor hidden_states_363_beta_0_to_fp16 = const()[name = tensor("hidden_states_363_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276476736)))]; + tensor var_8919_to_fp16 = const()[name = tensor("op_8919_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_363_cast_fp16 = layer_norm(axes = hidden_states_363_axes_0, beta = hidden_states_363_beta_0_to_fp16, epsilon = var_8919_to_fp16, gamma = hidden_states_363_gamma_0_to_fp16, x = inputs_267_cast_fp16)[name = tensor("hidden_states_363_cast_fp16")]; + tensor var_8934 = const()[name = tensor("op_8934"), val = tensor([1, 1])]; + tensor var_8936 = const()[name = tensor("op_8936"), val = tensor([1, 1])]; + tensor q_179_pad_type_0 = const()[name = tensor("q_179_pad_type_0"), val = tensor("custom")]; + tensor q_179_pad_0 = const()[name = tensor("q_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1276479360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1277708224))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_179_cast_fp16 = conv(dilations = var_8936, groups = var_6865, pad = q_179_pad_0, pad_type = q_179_pad_type_0, strides = var_8934, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_363_cast_fp16)[name = tensor("q_179_cast_fp16")]; + tensor var_8940 = const()[name = tensor("op_8940"), val = tensor([1, 1])]; + tensor var_8942 = const()[name = tensor("op_8942"), val = tensor([1, 1])]; + tensor k_179_pad_type_0 = const()[name = tensor("k_179_pad_type_0"), val = tensor("custom")]; + tensor k_179_pad_0 = const()[name = tensor("k_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1277708416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1279674560))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_179_cast_fp16 = conv(dilations = var_8942, groups = var_6865, pad = k_179_pad_0, pad_type = k_179_pad_type_0, strides = var_8940, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_179_cast_fp16")]; + tensor var_8946 = const()[name = tensor("op_8946"), val = tensor([1, 1])]; + tensor var_8948 = const()[name = tensor("op_8948"), val = tensor([1, 1])]; + tensor v_179_pad_type_0 = const()[name = tensor("v_179_pad_type_0"), val = tensor("custom")]; + tensor v_179_pad_0 = const()[name = tensor("v_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1279674752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281640896))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_179_cast_fp16 = conv(dilations = var_8948, groups = var_6865, pad = v_179_pad_0, pad_type = v_179_pad_type_0, strides = var_8946, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_179_cast_fp16")]; + tensor var_8952 = const()[name = tensor("op_8952"), val = tensor([1, 20, 64, -1])]; + tensor var_8953_cast_fp16 = reshape(shape = var_8952, x = q_179_cast_fp16)[name = tensor("op_8953_cast_fp16")]; + tensor var_8954 = const()[name = tensor("op_8954"), val = tensor([1, 20, 64, -1])]; + tensor var_8955_cast_fp16 = reshape(shape = var_8954, x = k_179_cast_fp16)[name = tensor("op_8955_cast_fp16")]; + tensor var_8956 = const()[name = tensor("op_8956"), val = tensor([1, 20, 64, -1])]; + tensor var_8957_cast_fp16 = reshape(shape = var_8956, x = v_179_cast_fp16)[name = tensor("op_8957_cast_fp16")]; + tensor attn_weights_357_transpose_x_0 = const()[name = tensor("attn_weights_357_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_357_transpose_y_0 = const()[name = tensor("attn_weights_357_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_357_cast_fp16 = matmul(transpose_x = attn_weights_357_transpose_x_0, transpose_y = attn_weights_357_transpose_y_0, x = var_8953_cast_fp16, y = var_8955_cast_fp16)[name = tensor("attn_weights_357_cast_fp16")]; + tensor attn_weights_359_cast_fp16 = mul(x = attn_weights_357_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_359_cast_fp16")]; + tensor var_8961_cast_fp16 = softmax(axis = var_6849, x = attn_weights_359_cast_fp16)[name = tensor("op_8961_cast_fp16")]; + tensor attn_179_transpose_x_0 = const()[name = tensor("attn_179_transpose_x_0"), val = tensor(false)]; + tensor attn_179_transpose_y_0 = const()[name = tensor("attn_179_transpose_y_0"), val = tensor(true)]; + tensor attn_179_cast_fp16 = matmul(transpose_x = attn_179_transpose_x_0, transpose_y = attn_179_transpose_y_0, x = var_8957_cast_fp16, y = var_8961_cast_fp16)[name = tensor("attn_179_cast_fp16")]; + tensor var_8965 = const()[name = tensor("op_8965"), val = tensor([1, 1280, 1, -1])]; + tensor input_539_cast_fp16 = reshape(shape = var_8965, x = attn_179_cast_fp16)[name = tensor("input_539_cast_fp16")]; + tensor var_8970 = const()[name = tensor("op_8970"), val = tensor([1, 1])]; + tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([1, 1])]; + tensor var_8974_pad_type_0 = const()[name = tensor("op_8974_pad_type_0"), val = tensor("custom")]; + tensor var_8974_pad_0 = const()[name = tensor("op_8974_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281641088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282869952))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282870144)))]; + tensor var_8974_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8972, groups = var_6865, pad = var_8974_pad_0, pad_type = var_8974_pad_type_0, strides = var_8970, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_539_cast_fp16)[name = tensor("op_8974_cast_fp16")]; + tensor inputs_269_cast_fp16 = add(x = var_8974_cast_fp16, y = inputs_267_cast_fp16)[name = tensor("inputs_269_cast_fp16")]; + tensor input_541_axes_0 = const()[name = tensor("input_541_axes_0"), val = tensor([1])]; + tensor input_541_gamma_0_to_fp16 = const()[name = tensor("input_541_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282872768)))]; + tensor input_541_beta_0_to_fp16 = const()[name = tensor("input_541_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282875392)))]; + tensor var_8984_to_fp16 = const()[name = tensor("op_8984_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = input_541_beta_0_to_fp16, epsilon = var_8984_to_fp16, gamma = input_541_gamma_0_to_fp16, x = inputs_269_cast_fp16)[name = tensor("input_541_cast_fp16")]; + tensor var_9000 = const()[name = tensor("op_9000"), val = tensor([1, 1])]; + tensor var_9002 = const()[name = tensor("op_9002"), val = tensor([1, 1])]; + tensor var_9004_pad_type_0 = const()[name = tensor("op_9004_pad_type_0"), val = tensor("custom")]; + tensor var_9004_pad_0 = const()[name = tensor("op_9004_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1282878016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292708480))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292708672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292716416))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9004_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9002, groups = var_6865, pad = var_9004_pad_0, pad_type = var_9004_pad_type_0, strides = var_9000, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_541_cast_fp16)[name = tensor("op_9004_cast_fp16")]; + tensor var_9005_split_sizes_0 = const()[name = tensor("op_9005_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9005_axis_0 = const()[name = tensor("op_9005_axis_0"), val = tensor(1)]; + tensor var_9005_cast_fp16_0, tensor var_9005_cast_fp16_1 = split(axis = var_9005_axis_0, split_sizes = var_9005_split_sizes_0, x = var_9004_cast_fp16)[name = tensor("op_9005_cast_fp16")]; + tensor var_9007_mode_0 = const()[name = tensor("op_9007_mode_0"), val = tensor("EXACT")]; + tensor var_9007_cast_fp16 = gelu(mode = var_9007_mode_0, x = var_9005_cast_fp16_1)[name = tensor("op_9007_cast_fp16")]; + tensor input_543_cast_fp16 = mul(x = var_9005_cast_fp16_0, y = var_9007_cast_fp16)[name = tensor("input_543_cast_fp16")]; + tensor var_9011 = const()[name = tensor("op_9011"), val = tensor([1, 1])]; + tensor var_9013 = const()[name = tensor("op_9013"), val = tensor([1, 1])]; + tensor var_9015_pad_type_0 = const()[name = tensor("op_9015_pad_type_0"), val = tensor("custom")]; + tensor var_9015_pad_0 = const()[name = tensor("op_9015_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1292716608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297631872))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297632064)))]; + tensor var_9015_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_9013, groups = var_6865, pad = var_9015_pad_0, pad_type = var_9015_pad_type_0, strides = var_9011, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_543_cast_fp16)[name = tensor("op_9015_cast_fp16")]; + tensor inputs_271_cast_fp16 = add(x = var_9015_cast_fp16, y = inputs_269_cast_fp16)[name = tensor("inputs_271_cast_fp16")]; + tensor hidden_states_367_axes_0 = const()[name = tensor("hidden_states_367_axes_0"), val = tensor([1])]; + tensor hidden_states_367_gamma_0_to_fp16 = const()[name = tensor("hidden_states_367_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297634688)))]; + tensor hidden_states_367_beta_0_to_fp16 = const()[name = tensor("hidden_states_367_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297637312)))]; + tensor var_9031_to_fp16 = const()[name = tensor("op_9031_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_367_cast_fp16 = layer_norm(axes = hidden_states_367_axes_0, beta = hidden_states_367_beta_0_to_fp16, epsilon = var_9031_to_fp16, gamma = hidden_states_367_gamma_0_to_fp16, x = inputs_271_cast_fp16)[name = tensor("hidden_states_367_cast_fp16")]; + tensor var_9046 = const()[name = tensor("op_9046"), val = tensor([1, 1])]; + tensor var_9048 = const()[name = tensor("op_9048"), val = tensor([1, 1])]; + tensor q_181_pad_type_0 = const()[name = tensor("q_181_pad_type_0"), val = tensor("custom")]; + tensor q_181_pad_0 = const()[name = tensor("q_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1297639936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298868800))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_181_cast_fp16 = conv(dilations = var_9048, groups = var_6865, pad = q_181_pad_0, pad_type = q_181_pad_type_0, strides = var_9046, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_367_cast_fp16)[name = tensor("q_181_cast_fp16")]; + tensor var_9052 = const()[name = tensor("op_9052"), val = tensor([1, 1])]; + tensor var_9054 = const()[name = tensor("op_9054"), val = tensor([1, 1])]; + tensor k_181_pad_type_0 = const()[name = tensor("k_181_pad_type_0"), val = tensor("custom")]; + tensor k_181_pad_0 = const()[name = tensor("k_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298868992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1300097856))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_181_cast_fp16 = conv(dilations = var_9054, groups = var_6865, pad = k_181_pad_0, pad_type = k_181_pad_type_0, strides = var_9052, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_367_cast_fp16)[name = tensor("k_181_cast_fp16")]; + tensor var_9058 = const()[name = tensor("op_9058"), val = tensor([1, 1])]; + tensor var_9060 = const()[name = tensor("op_9060"), val = tensor([1, 1])]; + tensor v_181_pad_type_0 = const()[name = tensor("v_181_pad_type_0"), val = tensor("custom")]; + tensor v_181_pad_0 = const()[name = tensor("v_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1300098048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1301326912))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_181_cast_fp16 = conv(dilations = var_9060, groups = var_6865, pad = v_181_pad_0, pad_type = v_181_pad_type_0, strides = var_9058, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_367_cast_fp16)[name = tensor("v_181_cast_fp16")]; + tensor var_9064 = const()[name = tensor("op_9064"), val = tensor([1, 20, 64, -1])]; + tensor var_9065_cast_fp16 = reshape(shape = var_9064, x = q_181_cast_fp16)[name = tensor("op_9065_cast_fp16")]; + tensor var_9066 = const()[name = tensor("op_9066"), val = tensor([1, 20, 64, -1])]; + tensor var_9067_cast_fp16 = reshape(shape = var_9066, x = k_181_cast_fp16)[name = tensor("op_9067_cast_fp16")]; + tensor var_9068 = const()[name = tensor("op_9068"), val = tensor([1, 20, 64, -1])]; + tensor var_9069_cast_fp16 = reshape(shape = var_9068, x = v_181_cast_fp16)[name = tensor("op_9069_cast_fp16")]; + tensor attn_weights_361_transpose_x_0 = const()[name = tensor("attn_weights_361_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_361_transpose_y_0 = const()[name = tensor("attn_weights_361_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_9065_cast_fp16, y = var_9067_cast_fp16)[name = tensor("attn_weights_361_cast_fp16")]; + tensor attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_363_cast_fp16")]; + tensor var_9073_cast_fp16 = softmax(axis = var_6849, x = attn_weights_363_cast_fp16)[name = tensor("op_9073_cast_fp16")]; + tensor attn_181_transpose_x_0 = const()[name = tensor("attn_181_transpose_x_0"), val = tensor(false)]; + tensor attn_181_transpose_y_0 = const()[name = tensor("attn_181_transpose_y_0"), val = tensor(true)]; + tensor attn_181_cast_fp16 = matmul(transpose_x = attn_181_transpose_x_0, transpose_y = attn_181_transpose_y_0, x = var_9069_cast_fp16, y = var_9073_cast_fp16)[name = tensor("attn_181_cast_fp16")]; + tensor var_9077 = const()[name = tensor("op_9077"), val = tensor([1, 1280, 1, -1])]; + tensor input_545_cast_fp16 = reshape(shape = var_9077, x = attn_181_cast_fp16)[name = tensor("input_545_cast_fp16")]; + tensor var_9082 = const()[name = tensor("op_9082"), val = tensor([1, 1])]; + tensor var_9084 = const()[name = tensor("op_9084"), val = tensor([1, 1])]; + tensor var_9086_pad_type_0 = const()[name = tensor("op_9086_pad_type_0"), val = tensor("custom")]; + tensor var_9086_pad_0 = const()[name = tensor("op_9086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1301327104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302555968))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302556160)))]; + tensor var_9086_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_9084, groups = var_6865, pad = var_9086_pad_0, pad_type = var_9086_pad_type_0, strides = var_9082, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_545_cast_fp16)[name = tensor("op_9086_cast_fp16")]; + tensor inputs_273_cast_fp16 = add(x = var_9086_cast_fp16, y = inputs_271_cast_fp16)[name = tensor("inputs_273_cast_fp16")]; + tensor hidden_states_369_axes_0 = const()[name = tensor("hidden_states_369_axes_0"), val = tensor([1])]; + tensor hidden_states_369_gamma_0_to_fp16 = const()[name = tensor("hidden_states_369_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302558784)))]; + tensor hidden_states_369_beta_0_to_fp16 = const()[name = tensor("hidden_states_369_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302561408)))]; + tensor var_9096_to_fp16 = const()[name = tensor("op_9096_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_369_cast_fp16 = layer_norm(axes = hidden_states_369_axes_0, beta = hidden_states_369_beta_0_to_fp16, epsilon = var_9096_to_fp16, gamma = hidden_states_369_gamma_0_to_fp16, x = inputs_273_cast_fp16)[name = tensor("hidden_states_369_cast_fp16")]; + tensor var_9111 = const()[name = tensor("op_9111"), val = tensor([1, 1])]; + tensor var_9113 = const()[name = tensor("op_9113"), val = tensor([1, 1])]; + tensor q_183_pad_type_0 = const()[name = tensor("q_183_pad_type_0"), val = tensor("custom")]; + tensor q_183_pad_0 = const()[name = tensor("q_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1302564032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1303792896))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_183_cast_fp16 = conv(dilations = var_9113, groups = var_6865, pad = q_183_pad_0, pad_type = q_183_pad_type_0, strides = var_9111, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_369_cast_fp16)[name = tensor("q_183_cast_fp16")]; + tensor var_9117 = const()[name = tensor("op_9117"), val = tensor([1, 1])]; + tensor var_9119 = const()[name = tensor("op_9119"), val = tensor([1, 1])]; + tensor k_183_pad_type_0 = const()[name = tensor("k_183_pad_type_0"), val = tensor("custom")]; + tensor k_183_pad_0 = const()[name = tensor("k_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1303793088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305759232))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_183_cast_fp16 = conv(dilations = var_9119, groups = var_6865, pad = k_183_pad_0, pad_type = k_183_pad_type_0, strides = var_9117, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_183_cast_fp16")]; + tensor var_9123 = const()[name = tensor("op_9123"), val = tensor([1, 1])]; + tensor var_9125 = const()[name = tensor("op_9125"), val = tensor([1, 1])]; + tensor v_183_pad_type_0 = const()[name = tensor("v_183_pad_type_0"), val = tensor("custom")]; + tensor v_183_pad_0 = const()[name = tensor("v_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305759424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307725568))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_183_cast_fp16 = conv(dilations = var_9125, groups = var_6865, pad = v_183_pad_0, pad_type = v_183_pad_type_0, strides = var_9123, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_183_cast_fp16")]; + tensor var_9129 = const()[name = tensor("op_9129"), val = tensor([1, 20, 64, -1])]; + tensor var_9130_cast_fp16 = reshape(shape = var_9129, x = q_183_cast_fp16)[name = tensor("op_9130_cast_fp16")]; + tensor var_9131 = const()[name = tensor("op_9131"), val = tensor([1, 20, 64, -1])]; + tensor var_9132_cast_fp16 = reshape(shape = var_9131, x = k_183_cast_fp16)[name = tensor("op_9132_cast_fp16")]; + tensor var_9133 = const()[name = tensor("op_9133"), val = tensor([1, 20, 64, -1])]; + tensor var_9134_cast_fp16 = reshape(shape = var_9133, x = v_183_cast_fp16)[name = tensor("op_9134_cast_fp16")]; + tensor attn_weights_365_transpose_x_0 = const()[name = tensor("attn_weights_365_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_365_transpose_y_0 = const()[name = tensor("attn_weights_365_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_365_cast_fp16 = matmul(transpose_x = attn_weights_365_transpose_x_0, transpose_y = attn_weights_365_transpose_y_0, x = var_9130_cast_fp16, y = var_9132_cast_fp16)[name = tensor("attn_weights_365_cast_fp16")]; + tensor attn_weights_367_cast_fp16 = mul(x = attn_weights_365_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_367_cast_fp16")]; + tensor var_9138_cast_fp16 = softmax(axis = var_6849, x = attn_weights_367_cast_fp16)[name = tensor("op_9138_cast_fp16")]; + tensor attn_183_transpose_x_0 = const()[name = tensor("attn_183_transpose_x_0"), val = tensor(false)]; + tensor attn_183_transpose_y_0 = const()[name = tensor("attn_183_transpose_y_0"), val = tensor(true)]; + tensor attn_183_cast_fp16 = matmul(transpose_x = attn_183_transpose_x_0, transpose_y = attn_183_transpose_y_0, x = var_9134_cast_fp16, y = var_9138_cast_fp16)[name = tensor("attn_183_cast_fp16")]; + tensor var_9142 = const()[name = tensor("op_9142"), val = tensor([1, 1280, 1, -1])]; + tensor input_547_cast_fp16 = reshape(shape = var_9142, x = attn_183_cast_fp16)[name = tensor("input_547_cast_fp16")]; + tensor var_9147 = const()[name = tensor("op_9147"), val = tensor([1, 1])]; + tensor var_9149 = const()[name = tensor("op_9149"), val = tensor([1, 1])]; + tensor var_9151_pad_type_0 = const()[name = tensor("op_9151_pad_type_0"), val = tensor("custom")]; + tensor var_9151_pad_0 = const()[name = tensor("op_9151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307725760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308954624))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308954816)))]; + tensor var_9151_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_9149, groups = var_6865, pad = var_9151_pad_0, pad_type = var_9151_pad_type_0, strides = var_9147, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = tensor("op_9151_cast_fp16")]; + tensor inputs_275_cast_fp16 = add(x = var_9151_cast_fp16, y = inputs_273_cast_fp16)[name = tensor("inputs_275_cast_fp16")]; + tensor input_549_axes_0 = const()[name = tensor("input_549_axes_0"), val = tensor([1])]; + tensor input_549_gamma_0_to_fp16 = const()[name = tensor("input_549_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308957440)))]; + tensor input_549_beta_0_to_fp16 = const()[name = tensor("input_549_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308960064)))]; + tensor var_9161_to_fp16 = const()[name = tensor("op_9161_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = input_549_beta_0_to_fp16, epsilon = var_9161_to_fp16, gamma = input_549_gamma_0_to_fp16, x = inputs_275_cast_fp16)[name = tensor("input_549_cast_fp16")]; + tensor var_9177 = const()[name = tensor("op_9177"), val = tensor([1, 1])]; + tensor var_9179 = const()[name = tensor("op_9179"), val = tensor([1, 1])]; + tensor var_9181_pad_type_0 = const()[name = tensor("op_9181_pad_type_0"), val = tensor("custom")]; + tensor var_9181_pad_0 = const()[name = tensor("op_9181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308962688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318793152))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318793344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318801088))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9181_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9179, groups = var_6865, pad = var_9181_pad_0, pad_type = var_9181_pad_type_0, strides = var_9177, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_549_cast_fp16)[name = tensor("op_9181_cast_fp16")]; + tensor var_9182_split_sizes_0 = const()[name = tensor("op_9182_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9182_axis_0 = const()[name = tensor("op_9182_axis_0"), val = tensor(1)]; + tensor var_9182_cast_fp16_0, tensor var_9182_cast_fp16_1 = split(axis = var_9182_axis_0, split_sizes = var_9182_split_sizes_0, x = var_9181_cast_fp16)[name = tensor("op_9182_cast_fp16")]; + tensor var_9184_mode_0 = const()[name = tensor("op_9184_mode_0"), val = tensor("EXACT")]; + tensor var_9184_cast_fp16 = gelu(mode = var_9184_mode_0, x = var_9182_cast_fp16_1)[name = tensor("op_9184_cast_fp16")]; + tensor input_551_cast_fp16 = mul(x = var_9182_cast_fp16_0, y = var_9184_cast_fp16)[name = tensor("input_551_cast_fp16")]; + tensor var_9188 = const()[name = tensor("op_9188"), val = tensor([1, 1])]; + tensor var_9190 = const()[name = tensor("op_9190"), val = tensor([1, 1])]; + tensor var_9192_pad_type_0 = const()[name = tensor("op_9192_pad_type_0"), val = tensor("custom")]; + tensor var_9192_pad_0 = const()[name = tensor("op_9192_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1318801280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323716544))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323716736)))]; + tensor var_9192_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_9190, groups = var_6865, pad = var_9192_pad_0, pad_type = var_9192_pad_type_0, strides = var_9188, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = tensor("op_9192_cast_fp16")]; + tensor inputs_277_cast_fp16 = add(x = var_9192_cast_fp16, y = inputs_275_cast_fp16)[name = tensor("inputs_277_cast_fp16")]; + tensor hidden_states_373_axes_0 = const()[name = tensor("hidden_states_373_axes_0"), val = tensor([1])]; + tensor hidden_states_373_gamma_0_to_fp16 = const()[name = tensor("hidden_states_373_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323719360)))]; + tensor hidden_states_373_beta_0_to_fp16 = const()[name = tensor("hidden_states_373_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323721984)))]; + tensor var_9208_to_fp16 = const()[name = tensor("op_9208_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_373_cast_fp16 = layer_norm(axes = hidden_states_373_axes_0, beta = hidden_states_373_beta_0_to_fp16, epsilon = var_9208_to_fp16, gamma = hidden_states_373_gamma_0_to_fp16, x = inputs_277_cast_fp16)[name = tensor("hidden_states_373_cast_fp16")]; + tensor var_9223 = const()[name = tensor("op_9223"), val = tensor([1, 1])]; + tensor var_9225 = const()[name = tensor("op_9225"), val = tensor([1, 1])]; + tensor q_185_pad_type_0 = const()[name = tensor("q_185_pad_type_0"), val = tensor("custom")]; + tensor q_185_pad_0 = const()[name = tensor("q_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323724608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1324953472))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_185_cast_fp16 = conv(dilations = var_9225, groups = var_6865, pad = q_185_pad_0, pad_type = q_185_pad_type_0, strides = var_9223, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_373_cast_fp16)[name = tensor("q_185_cast_fp16")]; + tensor var_9229 = const()[name = tensor("op_9229"), val = tensor([1, 1])]; + tensor var_9231 = const()[name = tensor("op_9231"), val = tensor([1, 1])]; + tensor k_185_pad_type_0 = const()[name = tensor("k_185_pad_type_0"), val = tensor("custom")]; + tensor k_185_pad_0 = const()[name = tensor("k_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1324953664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1326182528))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_185_cast_fp16 = conv(dilations = var_9231, groups = var_6865, pad = k_185_pad_0, pad_type = k_185_pad_type_0, strides = var_9229, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_373_cast_fp16)[name = tensor("k_185_cast_fp16")]; + tensor var_9235 = const()[name = tensor("op_9235"), val = tensor([1, 1])]; + tensor var_9237 = const()[name = tensor("op_9237"), val = tensor([1, 1])]; + tensor v_185_pad_type_0 = const()[name = tensor("v_185_pad_type_0"), val = tensor("custom")]; + tensor v_185_pad_0 = const()[name = tensor("v_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1326182720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1327411584))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_185_cast_fp16 = conv(dilations = var_9237, groups = var_6865, pad = v_185_pad_0, pad_type = v_185_pad_type_0, strides = var_9235, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_373_cast_fp16)[name = tensor("v_185_cast_fp16")]; + tensor var_9241 = const()[name = tensor("op_9241"), val = tensor([1, 20, 64, -1])]; + tensor var_9242_cast_fp16 = reshape(shape = var_9241, x = q_185_cast_fp16)[name = tensor("op_9242_cast_fp16")]; + tensor var_9243 = const()[name = tensor("op_9243"), val = tensor([1, 20, 64, -1])]; + tensor var_9244_cast_fp16 = reshape(shape = var_9243, x = k_185_cast_fp16)[name = tensor("op_9244_cast_fp16")]; + tensor var_9245 = const()[name = tensor("op_9245"), val = tensor([1, 20, 64, -1])]; + tensor var_9246_cast_fp16 = reshape(shape = var_9245, x = v_185_cast_fp16)[name = tensor("op_9246_cast_fp16")]; + tensor attn_weights_369_transpose_x_0 = const()[name = tensor("attn_weights_369_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_369_transpose_y_0 = const()[name = tensor("attn_weights_369_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_9242_cast_fp16, y = var_9244_cast_fp16)[name = tensor("attn_weights_369_cast_fp16")]; + tensor attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_371_cast_fp16")]; + tensor var_9250_cast_fp16 = softmax(axis = var_6849, x = attn_weights_371_cast_fp16)[name = tensor("op_9250_cast_fp16")]; + tensor attn_185_transpose_x_0 = const()[name = tensor("attn_185_transpose_x_0"), val = tensor(false)]; + tensor attn_185_transpose_y_0 = const()[name = tensor("attn_185_transpose_y_0"), val = tensor(true)]; + tensor attn_185_cast_fp16 = matmul(transpose_x = attn_185_transpose_x_0, transpose_y = attn_185_transpose_y_0, x = var_9246_cast_fp16, y = var_9250_cast_fp16)[name = tensor("attn_185_cast_fp16")]; + tensor var_9254 = const()[name = tensor("op_9254"), val = tensor([1, 1280, 1, -1])]; + tensor input_553_cast_fp16 = reshape(shape = var_9254, x = attn_185_cast_fp16)[name = tensor("input_553_cast_fp16")]; + tensor var_9259 = const()[name = tensor("op_9259"), val = tensor([1, 1])]; + tensor var_9261 = const()[name = tensor("op_9261"), val = tensor([1, 1])]; + tensor var_9263_pad_type_0 = const()[name = tensor("op_9263_pad_type_0"), val = tensor("custom")]; + tensor var_9263_pad_0 = const()[name = tensor("op_9263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1327411776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328640640))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328640832)))]; + tensor var_9263_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_9261, groups = var_6865, pad = var_9263_pad_0, pad_type = var_9263_pad_type_0, strides = var_9259, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_553_cast_fp16)[name = tensor("op_9263_cast_fp16")]; + tensor inputs_279_cast_fp16 = add(x = var_9263_cast_fp16, y = inputs_277_cast_fp16)[name = tensor("inputs_279_cast_fp16")]; + tensor hidden_states_375_axes_0 = const()[name = tensor("hidden_states_375_axes_0"), val = tensor([1])]; + tensor hidden_states_375_gamma_0_to_fp16 = const()[name = tensor("hidden_states_375_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328643456)))]; + tensor hidden_states_375_beta_0_to_fp16 = const()[name = tensor("hidden_states_375_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328646080)))]; + tensor var_9273_to_fp16 = const()[name = tensor("op_9273_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_375_cast_fp16 = layer_norm(axes = hidden_states_375_axes_0, beta = hidden_states_375_beta_0_to_fp16, epsilon = var_9273_to_fp16, gamma = hidden_states_375_gamma_0_to_fp16, x = inputs_279_cast_fp16)[name = tensor("hidden_states_375_cast_fp16")]; + tensor var_9288 = const()[name = tensor("op_9288"), val = tensor([1, 1])]; + tensor var_9290 = const()[name = tensor("op_9290"), val = tensor([1, 1])]; + tensor q_187_pad_type_0 = const()[name = tensor("q_187_pad_type_0"), val = tensor("custom")]; + tensor q_187_pad_0 = const()[name = tensor("q_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1328648704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1329877568))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_187_cast_fp16 = conv(dilations = var_9290, groups = var_6865, pad = q_187_pad_0, pad_type = q_187_pad_type_0, strides = var_9288, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_375_cast_fp16)[name = tensor("q_187_cast_fp16")]; + tensor var_9294 = const()[name = tensor("op_9294"), val = tensor([1, 1])]; + tensor var_9296 = const()[name = tensor("op_9296"), val = tensor([1, 1])]; + tensor k_187_pad_type_0 = const()[name = tensor("k_187_pad_type_0"), val = tensor("custom")]; + tensor k_187_pad_0 = const()[name = tensor("k_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1329877760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1331843904))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_187_cast_fp16 = conv(dilations = var_9296, groups = var_6865, pad = k_187_pad_0, pad_type = k_187_pad_type_0, strides = var_9294, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_187_cast_fp16")]; + tensor var_9300 = const()[name = tensor("op_9300"), val = tensor([1, 1])]; + tensor var_9302 = const()[name = tensor("op_9302"), val = tensor([1, 1])]; + tensor v_187_pad_type_0 = const()[name = tensor("v_187_pad_type_0"), val = tensor("custom")]; + tensor v_187_pad_0 = const()[name = tensor("v_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1331844096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1333810240))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_187_cast_fp16 = conv(dilations = var_9302, groups = var_6865, pad = v_187_pad_0, pad_type = v_187_pad_type_0, strides = var_9300, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_187_cast_fp16")]; + tensor var_9306 = const()[name = tensor("op_9306"), val = tensor([1, 20, 64, -1])]; + tensor var_9307_cast_fp16 = reshape(shape = var_9306, x = q_187_cast_fp16)[name = tensor("op_9307_cast_fp16")]; + tensor var_9308 = const()[name = tensor("op_9308"), val = tensor([1, 20, 64, -1])]; + tensor var_9309_cast_fp16 = reshape(shape = var_9308, x = k_187_cast_fp16)[name = tensor("op_9309_cast_fp16")]; + tensor var_9310 = const()[name = tensor("op_9310"), val = tensor([1, 20, 64, -1])]; + tensor var_9311_cast_fp16 = reshape(shape = var_9310, x = v_187_cast_fp16)[name = tensor("op_9311_cast_fp16")]; + tensor attn_weights_373_transpose_x_0 = const()[name = tensor("attn_weights_373_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_373_transpose_y_0 = const()[name = tensor("attn_weights_373_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_373_cast_fp16 = matmul(transpose_x = attn_weights_373_transpose_x_0, transpose_y = attn_weights_373_transpose_y_0, x = var_9307_cast_fp16, y = var_9309_cast_fp16)[name = tensor("attn_weights_373_cast_fp16")]; + tensor attn_weights_375_cast_fp16 = mul(x = attn_weights_373_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_375_cast_fp16")]; + tensor var_9315_cast_fp16 = softmax(axis = var_6849, x = attn_weights_375_cast_fp16)[name = tensor("op_9315_cast_fp16")]; + tensor attn_187_transpose_x_0 = const()[name = tensor("attn_187_transpose_x_0"), val = tensor(false)]; + tensor attn_187_transpose_y_0 = const()[name = tensor("attn_187_transpose_y_0"), val = tensor(true)]; + tensor attn_187_cast_fp16 = matmul(transpose_x = attn_187_transpose_x_0, transpose_y = attn_187_transpose_y_0, x = var_9311_cast_fp16, y = var_9315_cast_fp16)[name = tensor("attn_187_cast_fp16")]; + tensor var_9319 = const()[name = tensor("op_9319"), val = tensor([1, 1280, 1, -1])]; + tensor input_555_cast_fp16 = reshape(shape = var_9319, x = attn_187_cast_fp16)[name = tensor("input_555_cast_fp16")]; + tensor var_9324 = const()[name = tensor("op_9324"), val = tensor([1, 1])]; + tensor var_9326 = const()[name = tensor("op_9326"), val = tensor([1, 1])]; + tensor var_9328_pad_type_0 = const()[name = tensor("op_9328_pad_type_0"), val = tensor("custom")]; + tensor var_9328_pad_0 = const()[name = tensor("op_9328_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1333810432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335039296))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335039488)))]; + tensor var_9328_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_9326, groups = var_6865, pad = var_9328_pad_0, pad_type = var_9328_pad_type_0, strides = var_9324, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_555_cast_fp16)[name = tensor("op_9328_cast_fp16")]; + tensor inputs_281_cast_fp16 = add(x = var_9328_cast_fp16, y = inputs_279_cast_fp16)[name = tensor("inputs_281_cast_fp16")]; + tensor input_557_axes_0 = const()[name = tensor("input_557_axes_0"), val = tensor([1])]; + tensor input_557_gamma_0_to_fp16 = const()[name = tensor("input_557_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335042112)))]; + tensor input_557_beta_0_to_fp16 = const()[name = tensor("input_557_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335044736)))]; + tensor var_9338_to_fp16 = const()[name = tensor("op_9338_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_557_cast_fp16 = layer_norm(axes = input_557_axes_0, beta = input_557_beta_0_to_fp16, epsilon = var_9338_to_fp16, gamma = input_557_gamma_0_to_fp16, x = inputs_281_cast_fp16)[name = tensor("input_557_cast_fp16")]; + tensor var_9354 = const()[name = tensor("op_9354"), val = tensor([1, 1])]; + tensor var_9356 = const()[name = tensor("op_9356"), val = tensor([1, 1])]; + tensor var_9358_pad_type_0 = const()[name = tensor("op_9358_pad_type_0"), val = tensor("custom")]; + tensor var_9358_pad_0 = const()[name = tensor("op_9358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1335047360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344877824))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344878016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344885760))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9358_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9356, groups = var_6865, pad = var_9358_pad_0, pad_type = var_9358_pad_type_0, strides = var_9354, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_557_cast_fp16)[name = tensor("op_9358_cast_fp16")]; + tensor var_9359_split_sizes_0 = const()[name = tensor("op_9359_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9359_axis_0 = const()[name = tensor("op_9359_axis_0"), val = tensor(1)]; + tensor var_9359_cast_fp16_0, tensor var_9359_cast_fp16_1 = split(axis = var_9359_axis_0, split_sizes = var_9359_split_sizes_0, x = var_9358_cast_fp16)[name = tensor("op_9359_cast_fp16")]; + tensor var_9361_mode_0 = const()[name = tensor("op_9361_mode_0"), val = tensor("EXACT")]; + tensor var_9361_cast_fp16 = gelu(mode = var_9361_mode_0, x = var_9359_cast_fp16_1)[name = tensor("op_9361_cast_fp16")]; + tensor input_559_cast_fp16 = mul(x = var_9359_cast_fp16_0, y = var_9361_cast_fp16)[name = tensor("input_559_cast_fp16")]; + tensor var_9365 = const()[name = tensor("op_9365"), val = tensor([1, 1])]; + tensor var_9367 = const()[name = tensor("op_9367"), val = tensor([1, 1])]; + tensor var_9369_pad_type_0 = const()[name = tensor("op_9369_pad_type_0"), val = tensor("custom")]; + tensor var_9369_pad_0 = const()[name = tensor("op_9369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1344885952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349801216))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349801408)))]; + tensor var_9369_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_9367, groups = var_6865, pad = var_9369_pad_0, pad_type = var_9369_pad_type_0, strides = var_9365, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_559_cast_fp16)[name = tensor("op_9369_cast_fp16")]; + tensor inputs_283_cast_fp16 = add(x = var_9369_cast_fp16, y = inputs_281_cast_fp16)[name = tensor("inputs_283_cast_fp16")]; + tensor hidden_states_379_axes_0 = const()[name = tensor("hidden_states_379_axes_0"), val = tensor([1])]; + tensor hidden_states_379_gamma_0_to_fp16 = const()[name = tensor("hidden_states_379_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349804032)))]; + tensor hidden_states_379_beta_0_to_fp16 = const()[name = tensor("hidden_states_379_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349806656)))]; + tensor var_9385_to_fp16 = const()[name = tensor("op_9385_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_379_cast_fp16 = layer_norm(axes = hidden_states_379_axes_0, beta = hidden_states_379_beta_0_to_fp16, epsilon = var_9385_to_fp16, gamma = hidden_states_379_gamma_0_to_fp16, x = inputs_283_cast_fp16)[name = tensor("hidden_states_379_cast_fp16")]; + tensor var_9400 = const()[name = tensor("op_9400"), val = tensor([1, 1])]; + tensor var_9402 = const()[name = tensor("op_9402"), val = tensor([1, 1])]; + tensor q_189_pad_type_0 = const()[name = tensor("q_189_pad_type_0"), val = tensor("custom")]; + tensor q_189_pad_0 = const()[name = tensor("q_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349809280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1351038144))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_189_cast_fp16 = conv(dilations = var_9402, groups = var_6865, pad = q_189_pad_0, pad_type = q_189_pad_type_0, strides = var_9400, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_379_cast_fp16)[name = tensor("q_189_cast_fp16")]; + tensor var_9406 = const()[name = tensor("op_9406"), val = tensor([1, 1])]; + tensor var_9408 = const()[name = tensor("op_9408"), val = tensor([1, 1])]; + tensor k_189_pad_type_0 = const()[name = tensor("k_189_pad_type_0"), val = tensor("custom")]; + tensor k_189_pad_0 = const()[name = tensor("k_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1351038336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1352267200))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_189_cast_fp16 = conv(dilations = var_9408, groups = var_6865, pad = k_189_pad_0, pad_type = k_189_pad_type_0, strides = var_9406, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_379_cast_fp16)[name = tensor("k_189_cast_fp16")]; + tensor var_9412 = const()[name = tensor("op_9412"), val = tensor([1, 1])]; + tensor var_9414 = const()[name = tensor("op_9414"), val = tensor([1, 1])]; + tensor v_189_pad_type_0 = const()[name = tensor("v_189_pad_type_0"), val = tensor("custom")]; + tensor v_189_pad_0 = const()[name = tensor("v_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1352267392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1353496256))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_189_cast_fp16 = conv(dilations = var_9414, groups = var_6865, pad = v_189_pad_0, pad_type = v_189_pad_type_0, strides = var_9412, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_379_cast_fp16)[name = tensor("v_189_cast_fp16")]; + tensor var_9418 = const()[name = tensor("op_9418"), val = tensor([1, 20, 64, -1])]; + tensor var_9419_cast_fp16 = reshape(shape = var_9418, x = q_189_cast_fp16)[name = tensor("op_9419_cast_fp16")]; + tensor var_9420 = const()[name = tensor("op_9420"), val = tensor([1, 20, 64, -1])]; + tensor var_9421_cast_fp16 = reshape(shape = var_9420, x = k_189_cast_fp16)[name = tensor("op_9421_cast_fp16")]; + tensor var_9422 = const()[name = tensor("op_9422"), val = tensor([1, 20, 64, -1])]; + tensor var_9423_cast_fp16 = reshape(shape = var_9422, x = v_189_cast_fp16)[name = tensor("op_9423_cast_fp16")]; + tensor attn_weights_377_transpose_x_0 = const()[name = tensor("attn_weights_377_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_377_transpose_y_0 = const()[name = tensor("attn_weights_377_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_9419_cast_fp16, y = var_9421_cast_fp16)[name = tensor("attn_weights_377_cast_fp16")]; + tensor attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_379_cast_fp16")]; + tensor var_9427_cast_fp16 = softmax(axis = var_6849, x = attn_weights_379_cast_fp16)[name = tensor("op_9427_cast_fp16")]; + tensor attn_189_transpose_x_0 = const()[name = tensor("attn_189_transpose_x_0"), val = tensor(false)]; + tensor attn_189_transpose_y_0 = const()[name = tensor("attn_189_transpose_y_0"), val = tensor(true)]; + tensor attn_189_cast_fp16 = matmul(transpose_x = attn_189_transpose_x_0, transpose_y = attn_189_transpose_y_0, x = var_9423_cast_fp16, y = var_9427_cast_fp16)[name = tensor("attn_189_cast_fp16")]; + tensor var_9431 = const()[name = tensor("op_9431"), val = tensor([1, 1280, 1, -1])]; + tensor input_561_cast_fp16 = reshape(shape = var_9431, x = attn_189_cast_fp16)[name = tensor("input_561_cast_fp16")]; + tensor var_9436 = const()[name = tensor("op_9436"), val = tensor([1, 1])]; + tensor var_9438 = const()[name = tensor("op_9438"), val = tensor([1, 1])]; + tensor var_9440_pad_type_0 = const()[name = tensor("op_9440_pad_type_0"), val = tensor("custom")]; + tensor var_9440_pad_0 = const()[name = tensor("op_9440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1353496448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354725312))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354725504)))]; + tensor var_9440_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_9438, groups = var_6865, pad = var_9440_pad_0, pad_type = var_9440_pad_type_0, strides = var_9436, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_561_cast_fp16)[name = tensor("op_9440_cast_fp16")]; + tensor inputs_285_cast_fp16 = add(x = var_9440_cast_fp16, y = inputs_283_cast_fp16)[name = tensor("inputs_285_cast_fp16")]; + tensor hidden_states_381_axes_0 = const()[name = tensor("hidden_states_381_axes_0"), val = tensor([1])]; + tensor hidden_states_381_gamma_0_to_fp16 = const()[name = tensor("hidden_states_381_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354728128)))]; + tensor hidden_states_381_beta_0_to_fp16 = const()[name = tensor("hidden_states_381_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354730752)))]; + tensor var_9450_to_fp16 = const()[name = tensor("op_9450_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_381_cast_fp16 = layer_norm(axes = hidden_states_381_axes_0, beta = hidden_states_381_beta_0_to_fp16, epsilon = var_9450_to_fp16, gamma = hidden_states_381_gamma_0_to_fp16, x = inputs_285_cast_fp16)[name = tensor("hidden_states_381_cast_fp16")]; + tensor var_9465 = const()[name = tensor("op_9465"), val = tensor([1, 1])]; + tensor var_9467 = const()[name = tensor("op_9467"), val = tensor([1, 1])]; + tensor q_191_pad_type_0 = const()[name = tensor("q_191_pad_type_0"), val = tensor("custom")]; + tensor q_191_pad_0 = const()[name = tensor("q_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1354733376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1355962240))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_191_cast_fp16 = conv(dilations = var_9467, groups = var_6865, pad = q_191_pad_0, pad_type = q_191_pad_type_0, strides = var_9465, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_381_cast_fp16)[name = tensor("q_191_cast_fp16")]; + tensor var_9471 = const()[name = tensor("op_9471"), val = tensor([1, 1])]; + tensor var_9473 = const()[name = tensor("op_9473"), val = tensor([1, 1])]; + tensor k_191_pad_type_0 = const()[name = tensor("k_191_pad_type_0"), val = tensor("custom")]; + tensor k_191_pad_0 = const()[name = tensor("k_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1355962432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1357928576))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_191_cast_fp16 = conv(dilations = var_9473, groups = var_6865, pad = k_191_pad_0, pad_type = k_191_pad_type_0, strides = var_9471, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_191_cast_fp16")]; + tensor var_9477 = const()[name = tensor("op_9477"), val = tensor([1, 1])]; + tensor var_9479 = const()[name = tensor("op_9479"), val = tensor([1, 1])]; + tensor v_191_pad_type_0 = const()[name = tensor("v_191_pad_type_0"), val = tensor("custom")]; + tensor v_191_pad_0 = const()[name = tensor("v_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1357928768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359894912))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_191_cast_fp16 = conv(dilations = var_9479, groups = var_6865, pad = v_191_pad_0, pad_type = v_191_pad_type_0, strides = var_9477, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_191_cast_fp16")]; + tensor var_9483 = const()[name = tensor("op_9483"), val = tensor([1, 20, 64, -1])]; + tensor var_9484_cast_fp16 = reshape(shape = var_9483, x = q_191_cast_fp16)[name = tensor("op_9484_cast_fp16")]; + tensor var_9485 = const()[name = tensor("op_9485"), val = tensor([1, 20, 64, -1])]; + tensor var_9486_cast_fp16 = reshape(shape = var_9485, x = k_191_cast_fp16)[name = tensor("op_9486_cast_fp16")]; + tensor var_9487 = const()[name = tensor("op_9487"), val = tensor([1, 20, 64, -1])]; + tensor var_9488_cast_fp16 = reshape(shape = var_9487, x = v_191_cast_fp16)[name = tensor("op_9488_cast_fp16")]; + tensor attn_weights_381_transpose_x_0 = const()[name = tensor("attn_weights_381_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_381_transpose_y_0 = const()[name = tensor("attn_weights_381_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_381_cast_fp16 = matmul(transpose_x = attn_weights_381_transpose_x_0, transpose_y = attn_weights_381_transpose_y_0, x = var_9484_cast_fp16, y = var_9486_cast_fp16)[name = tensor("attn_weights_381_cast_fp16")]; + tensor attn_weights_383_cast_fp16 = mul(x = attn_weights_381_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_383_cast_fp16")]; + tensor var_9492_cast_fp16 = softmax(axis = var_6849, x = attn_weights_383_cast_fp16)[name = tensor("op_9492_cast_fp16")]; + tensor attn_191_transpose_x_0 = const()[name = tensor("attn_191_transpose_x_0"), val = tensor(false)]; + tensor attn_191_transpose_y_0 = const()[name = tensor("attn_191_transpose_y_0"), val = tensor(true)]; + tensor attn_191_cast_fp16 = matmul(transpose_x = attn_191_transpose_x_0, transpose_y = attn_191_transpose_y_0, x = var_9488_cast_fp16, y = var_9492_cast_fp16)[name = tensor("attn_191_cast_fp16")]; + tensor var_9496 = const()[name = tensor("op_9496"), val = tensor([1, 1280, 1, -1])]; + tensor input_563_cast_fp16 = reshape(shape = var_9496, x = attn_191_cast_fp16)[name = tensor("input_563_cast_fp16")]; + tensor var_9501 = const()[name = tensor("op_9501"), val = tensor([1, 1])]; + tensor var_9503 = const()[name = tensor("op_9503"), val = tensor([1, 1])]; + tensor var_9505_pad_type_0 = const()[name = tensor("op_9505_pad_type_0"), val = tensor("custom")]; + tensor var_9505_pad_0 = const()[name = tensor("op_9505_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359895104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361123968))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361124160)))]; + tensor var_9505_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_9503, groups = var_6865, pad = var_9505_pad_0, pad_type = var_9505_pad_type_0, strides = var_9501, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = tensor("op_9505_cast_fp16")]; + tensor inputs_287_cast_fp16 = add(x = var_9505_cast_fp16, y = inputs_285_cast_fp16)[name = tensor("inputs_287_cast_fp16")]; + tensor input_565_axes_0 = const()[name = tensor("input_565_axes_0"), val = tensor([1])]; + tensor input_565_gamma_0_to_fp16 = const()[name = tensor("input_565_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361126784)))]; + tensor input_565_beta_0_to_fp16 = const()[name = tensor("input_565_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361129408)))]; + tensor var_9515_to_fp16 = const()[name = tensor("op_9515_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_565_cast_fp16 = layer_norm(axes = input_565_axes_0, beta = input_565_beta_0_to_fp16, epsilon = var_9515_to_fp16, gamma = input_565_gamma_0_to_fp16, x = inputs_287_cast_fp16)[name = tensor("input_565_cast_fp16")]; + tensor var_9531 = const()[name = tensor("op_9531"), val = tensor([1, 1])]; + tensor var_9533 = const()[name = tensor("op_9533"), val = tensor([1, 1])]; + tensor var_9535_pad_type_0 = const()[name = tensor("op_9535_pad_type_0"), val = tensor("custom")]; + tensor var_9535_pad_0 = const()[name = tensor("op_9535_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1361132032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370962496))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370962688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370970432))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9535_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9533, groups = var_6865, pad = var_9535_pad_0, pad_type = var_9535_pad_type_0, strides = var_9531, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_565_cast_fp16)[name = tensor("op_9535_cast_fp16")]; + tensor var_9536_split_sizes_0 = const()[name = tensor("op_9536_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9536_axis_0 = const()[name = tensor("op_9536_axis_0"), val = tensor(1)]; + tensor var_9536_cast_fp16_0, tensor var_9536_cast_fp16_1 = split(axis = var_9536_axis_0, split_sizes = var_9536_split_sizes_0, x = var_9535_cast_fp16)[name = tensor("op_9536_cast_fp16")]; + tensor var_9538_mode_0 = const()[name = tensor("op_9538_mode_0"), val = tensor("EXACT")]; + tensor var_9538_cast_fp16 = gelu(mode = var_9538_mode_0, x = var_9536_cast_fp16_1)[name = tensor("op_9538_cast_fp16")]; + tensor input_567_cast_fp16 = mul(x = var_9536_cast_fp16_0, y = var_9538_cast_fp16)[name = tensor("input_567_cast_fp16")]; + tensor var_9542 = const()[name = tensor("op_9542"), val = tensor([1, 1])]; + tensor var_9544 = const()[name = tensor("op_9544"), val = tensor([1, 1])]; + tensor var_9546_pad_type_0 = const()[name = tensor("op_9546_pad_type_0"), val = tensor("custom")]; + tensor var_9546_pad_0 = const()[name = tensor("op_9546_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1370970624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375885888))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375886080)))]; + tensor var_9546_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_9544, groups = var_6865, pad = var_9546_pad_0, pad_type = var_9546_pad_type_0, strides = var_9542, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_567_cast_fp16)[name = tensor("op_9546_cast_fp16")]; + tensor inputs_289_cast_fp16 = add(x = var_9546_cast_fp16, y = inputs_287_cast_fp16)[name = tensor("inputs_289_cast_fp16")]; + tensor hidden_states_385_axes_0 = const()[name = tensor("hidden_states_385_axes_0"), val = tensor([1])]; + tensor hidden_states_385_gamma_0_to_fp16 = const()[name = tensor("hidden_states_385_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375888704)))]; + tensor hidden_states_385_beta_0_to_fp16 = const()[name = tensor("hidden_states_385_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375891328)))]; + tensor var_9562_to_fp16 = const()[name = tensor("op_9562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_385_cast_fp16 = layer_norm(axes = hidden_states_385_axes_0, beta = hidden_states_385_beta_0_to_fp16, epsilon = var_9562_to_fp16, gamma = hidden_states_385_gamma_0_to_fp16, x = inputs_289_cast_fp16)[name = tensor("hidden_states_385_cast_fp16")]; + tensor var_9577 = const()[name = tensor("op_9577"), val = tensor([1, 1])]; + tensor var_9579 = const()[name = tensor("op_9579"), val = tensor([1, 1])]; + tensor q_193_pad_type_0 = const()[name = tensor("q_193_pad_type_0"), val = tensor("custom")]; + tensor q_193_pad_0 = const()[name = tensor("q_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1375893952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1377122816))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_193_cast_fp16 = conv(dilations = var_9579, groups = var_6865, pad = q_193_pad_0, pad_type = q_193_pad_type_0, strides = var_9577, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_385_cast_fp16)[name = tensor("q_193_cast_fp16")]; + tensor var_9583 = const()[name = tensor("op_9583"), val = tensor([1, 1])]; + tensor var_9585 = const()[name = tensor("op_9585"), val = tensor([1, 1])]; + tensor k_193_pad_type_0 = const()[name = tensor("k_193_pad_type_0"), val = tensor("custom")]; + tensor k_193_pad_0 = const()[name = tensor("k_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1377123008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1378351872))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_193_cast_fp16 = conv(dilations = var_9585, groups = var_6865, pad = k_193_pad_0, pad_type = k_193_pad_type_0, strides = var_9583, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_385_cast_fp16)[name = tensor("k_193_cast_fp16")]; + tensor var_9589 = const()[name = tensor("op_9589"), val = tensor([1, 1])]; + tensor var_9591 = const()[name = tensor("op_9591"), val = tensor([1, 1])]; + tensor v_193_pad_type_0 = const()[name = tensor("v_193_pad_type_0"), val = tensor("custom")]; + tensor v_193_pad_0 = const()[name = tensor("v_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1378352064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379580928))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_193_cast_fp16 = conv(dilations = var_9591, groups = var_6865, pad = v_193_pad_0, pad_type = v_193_pad_type_0, strides = var_9589, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_385_cast_fp16)[name = tensor("v_193_cast_fp16")]; + tensor var_9595 = const()[name = tensor("op_9595"), val = tensor([1, 20, 64, -1])]; + tensor var_9596_cast_fp16 = reshape(shape = var_9595, x = q_193_cast_fp16)[name = tensor("op_9596_cast_fp16")]; + tensor var_9597 = const()[name = tensor("op_9597"), val = tensor([1, 20, 64, -1])]; + tensor var_9598_cast_fp16 = reshape(shape = var_9597, x = k_193_cast_fp16)[name = tensor("op_9598_cast_fp16")]; + tensor var_9599 = const()[name = tensor("op_9599"), val = tensor([1, 20, 64, -1])]; + tensor var_9600_cast_fp16 = reshape(shape = var_9599, x = v_193_cast_fp16)[name = tensor("op_9600_cast_fp16")]; + tensor attn_weights_385_transpose_x_0 = const()[name = tensor("attn_weights_385_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_385_transpose_y_0 = const()[name = tensor("attn_weights_385_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_9596_cast_fp16, y = var_9598_cast_fp16)[name = tensor("attn_weights_385_cast_fp16")]; + tensor attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_387_cast_fp16")]; + tensor var_9604_cast_fp16 = softmax(axis = var_6849, x = attn_weights_387_cast_fp16)[name = tensor("op_9604_cast_fp16")]; + tensor attn_193_transpose_x_0 = const()[name = tensor("attn_193_transpose_x_0"), val = tensor(false)]; + tensor attn_193_transpose_y_0 = const()[name = tensor("attn_193_transpose_y_0"), val = tensor(true)]; + tensor attn_193_cast_fp16 = matmul(transpose_x = attn_193_transpose_x_0, transpose_y = attn_193_transpose_y_0, x = var_9600_cast_fp16, y = var_9604_cast_fp16)[name = tensor("attn_193_cast_fp16")]; + tensor var_9608 = const()[name = tensor("op_9608"), val = tensor([1, 1280, 1, -1])]; + tensor input_569_cast_fp16 = reshape(shape = var_9608, x = attn_193_cast_fp16)[name = tensor("input_569_cast_fp16")]; + tensor var_9613 = const()[name = tensor("op_9613"), val = tensor([1, 1])]; + tensor var_9615 = const()[name = tensor("op_9615"), val = tensor([1, 1])]; + tensor var_9617_pad_type_0 = const()[name = tensor("op_9617_pad_type_0"), val = tensor("custom")]; + tensor var_9617_pad_0 = const()[name = tensor("op_9617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379581120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380809984))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380810176)))]; + tensor var_9617_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_9615, groups = var_6865, pad = var_9617_pad_0, pad_type = var_9617_pad_type_0, strides = var_9613, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_569_cast_fp16)[name = tensor("op_9617_cast_fp16")]; + tensor inputs_291_cast_fp16 = add(x = var_9617_cast_fp16, y = inputs_289_cast_fp16)[name = tensor("inputs_291_cast_fp16")]; + tensor hidden_states_387_axes_0 = const()[name = tensor("hidden_states_387_axes_0"), val = tensor([1])]; + tensor hidden_states_387_gamma_0_to_fp16 = const()[name = tensor("hidden_states_387_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380812800)))]; + tensor hidden_states_387_beta_0_to_fp16 = const()[name = tensor("hidden_states_387_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380815424)))]; + tensor var_9627_to_fp16 = const()[name = tensor("op_9627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_387_cast_fp16 = layer_norm(axes = hidden_states_387_axes_0, beta = hidden_states_387_beta_0_to_fp16, epsilon = var_9627_to_fp16, gamma = hidden_states_387_gamma_0_to_fp16, x = inputs_291_cast_fp16)[name = tensor("hidden_states_387_cast_fp16")]; + tensor var_9642 = const()[name = tensor("op_9642"), val = tensor([1, 1])]; + tensor var_9644 = const()[name = tensor("op_9644"), val = tensor([1, 1])]; + tensor q_195_pad_type_0 = const()[name = tensor("q_195_pad_type_0"), val = tensor("custom")]; + tensor q_195_pad_0 = const()[name = tensor("q_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1380818048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1382046912))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_195_cast_fp16 = conv(dilations = var_9644, groups = var_6865, pad = q_195_pad_0, pad_type = q_195_pad_type_0, strides = var_9642, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_387_cast_fp16)[name = tensor("q_195_cast_fp16")]; + tensor var_9648 = const()[name = tensor("op_9648"), val = tensor([1, 1])]; + tensor var_9650 = const()[name = tensor("op_9650"), val = tensor([1, 1])]; + tensor k_195_pad_type_0 = const()[name = tensor("k_195_pad_type_0"), val = tensor("custom")]; + tensor k_195_pad_0 = const()[name = tensor("k_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1382047104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1384013248))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_195_cast_fp16 = conv(dilations = var_9650, groups = var_6865, pad = k_195_pad_0, pad_type = k_195_pad_type_0, strides = var_9648, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_195_cast_fp16")]; + tensor var_9654 = const()[name = tensor("op_9654"), val = tensor([1, 1])]; + tensor var_9656 = const()[name = tensor("op_9656"), val = tensor([1, 1])]; + tensor v_195_pad_type_0 = const()[name = tensor("v_195_pad_type_0"), val = tensor("custom")]; + tensor v_195_pad_0 = const()[name = tensor("v_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1384013440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1385979584))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_195_cast_fp16 = conv(dilations = var_9656, groups = var_6865, pad = v_195_pad_0, pad_type = v_195_pad_type_0, strides = var_9654, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_195_cast_fp16")]; + tensor var_9660 = const()[name = tensor("op_9660"), val = tensor([1, 20, 64, -1])]; + tensor var_9661_cast_fp16 = reshape(shape = var_9660, x = q_195_cast_fp16)[name = tensor("op_9661_cast_fp16")]; + tensor var_9662 = const()[name = tensor("op_9662"), val = tensor([1, 20, 64, -1])]; + tensor var_9663_cast_fp16 = reshape(shape = var_9662, x = k_195_cast_fp16)[name = tensor("op_9663_cast_fp16")]; + tensor var_9664 = const()[name = tensor("op_9664"), val = tensor([1, 20, 64, -1])]; + tensor var_9665_cast_fp16 = reshape(shape = var_9664, x = v_195_cast_fp16)[name = tensor("op_9665_cast_fp16")]; + tensor attn_weights_389_transpose_x_0 = const()[name = tensor("attn_weights_389_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_389_transpose_y_0 = const()[name = tensor("attn_weights_389_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_389_cast_fp16 = matmul(transpose_x = attn_weights_389_transpose_x_0, transpose_y = attn_weights_389_transpose_y_0, x = var_9661_cast_fp16, y = var_9663_cast_fp16)[name = tensor("attn_weights_389_cast_fp16")]; + tensor attn_weights_391_cast_fp16 = mul(x = attn_weights_389_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_391_cast_fp16")]; + tensor var_9669_cast_fp16 = softmax(axis = var_6849, x = attn_weights_391_cast_fp16)[name = tensor("op_9669_cast_fp16")]; + tensor attn_195_transpose_x_0 = const()[name = tensor("attn_195_transpose_x_0"), val = tensor(false)]; + tensor attn_195_transpose_y_0 = const()[name = tensor("attn_195_transpose_y_0"), val = tensor(true)]; + tensor attn_195_cast_fp16 = matmul(transpose_x = attn_195_transpose_x_0, transpose_y = attn_195_transpose_y_0, x = var_9665_cast_fp16, y = var_9669_cast_fp16)[name = tensor("attn_195_cast_fp16")]; + tensor var_9673 = const()[name = tensor("op_9673"), val = tensor([1, 1280, 1, -1])]; + tensor input_571_cast_fp16 = reshape(shape = var_9673, x = attn_195_cast_fp16)[name = tensor("input_571_cast_fp16")]; + tensor var_9678 = const()[name = tensor("op_9678"), val = tensor([1, 1])]; + tensor var_9680 = const()[name = tensor("op_9680"), val = tensor([1, 1])]; + tensor var_9682_pad_type_0 = const()[name = tensor("op_9682_pad_type_0"), val = tensor("custom")]; + tensor var_9682_pad_0 = const()[name = tensor("op_9682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1385979776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387208640))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387208832)))]; + tensor var_9682_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_9680, groups = var_6865, pad = var_9682_pad_0, pad_type = var_9682_pad_type_0, strides = var_9678, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_571_cast_fp16)[name = tensor("op_9682_cast_fp16")]; + tensor inputs_293_cast_fp16 = add(x = var_9682_cast_fp16, y = inputs_291_cast_fp16)[name = tensor("inputs_293_cast_fp16")]; + tensor input_573_axes_0 = const()[name = tensor("input_573_axes_0"), val = tensor([1])]; + tensor input_573_gamma_0_to_fp16 = const()[name = tensor("input_573_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387211456)))]; + tensor input_573_beta_0_to_fp16 = const()[name = tensor("input_573_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387214080)))]; + tensor var_9692_to_fp16 = const()[name = tensor("op_9692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_573_cast_fp16 = layer_norm(axes = input_573_axes_0, beta = input_573_beta_0_to_fp16, epsilon = var_9692_to_fp16, gamma = input_573_gamma_0_to_fp16, x = inputs_293_cast_fp16)[name = tensor("input_573_cast_fp16")]; + tensor var_9708 = const()[name = tensor("op_9708"), val = tensor([1, 1])]; + tensor var_9710 = const()[name = tensor("op_9710"), val = tensor([1, 1])]; + tensor var_9712_pad_type_0 = const()[name = tensor("op_9712_pad_type_0"), val = tensor("custom")]; + tensor var_9712_pad_0 = const()[name = tensor("op_9712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1387216704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397047168))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397047360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397055104))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9712_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9710, groups = var_6865, pad = var_9712_pad_0, pad_type = var_9712_pad_type_0, strides = var_9708, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_573_cast_fp16)[name = tensor("op_9712_cast_fp16")]; + tensor var_9713_split_sizes_0 = const()[name = tensor("op_9713_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9713_axis_0 = const()[name = tensor("op_9713_axis_0"), val = tensor(1)]; + tensor var_9713_cast_fp16_0, tensor var_9713_cast_fp16_1 = split(axis = var_9713_axis_0, split_sizes = var_9713_split_sizes_0, x = var_9712_cast_fp16)[name = tensor("op_9713_cast_fp16")]; + tensor var_9715_mode_0 = const()[name = tensor("op_9715_mode_0"), val = tensor("EXACT")]; + tensor var_9715_cast_fp16 = gelu(mode = var_9715_mode_0, x = var_9713_cast_fp16_1)[name = tensor("op_9715_cast_fp16")]; + tensor input_575_cast_fp16 = mul(x = var_9713_cast_fp16_0, y = var_9715_cast_fp16)[name = tensor("input_575_cast_fp16")]; + tensor var_9719 = const()[name = tensor("op_9719"), val = tensor([1, 1])]; + tensor var_9721 = const()[name = tensor("op_9721"), val = tensor([1, 1])]; + tensor var_9723_pad_type_0 = const()[name = tensor("op_9723_pad_type_0"), val = tensor("custom")]; + tensor var_9723_pad_0 = const()[name = tensor("op_9723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1397055296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401970560))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401970752)))]; + tensor var_9723_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_9721, groups = var_6865, pad = var_9723_pad_0, pad_type = var_9723_pad_type_0, strides = var_9719, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_575_cast_fp16)[name = tensor("op_9723_cast_fp16")]; + tensor inputs_295_cast_fp16 = add(x = var_9723_cast_fp16, y = inputs_293_cast_fp16)[name = tensor("inputs_295_cast_fp16")]; + tensor hidden_states_391_axes_0 = const()[name = tensor("hidden_states_391_axes_0"), val = tensor([1])]; + tensor hidden_states_391_gamma_0_to_fp16 = const()[name = tensor("hidden_states_391_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401973376)))]; + tensor hidden_states_391_beta_0_to_fp16 = const()[name = tensor("hidden_states_391_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401976000)))]; + tensor var_9739_to_fp16 = const()[name = tensor("op_9739_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_391_cast_fp16 = layer_norm(axes = hidden_states_391_axes_0, beta = hidden_states_391_beta_0_to_fp16, epsilon = var_9739_to_fp16, gamma = hidden_states_391_gamma_0_to_fp16, x = inputs_295_cast_fp16)[name = tensor("hidden_states_391_cast_fp16")]; + tensor var_9754 = const()[name = tensor("op_9754"), val = tensor([1, 1])]; + tensor var_9756 = const()[name = tensor("op_9756"), val = tensor([1, 1])]; + tensor q_197_pad_type_0 = const()[name = tensor("q_197_pad_type_0"), val = tensor("custom")]; + tensor q_197_pad_0 = const()[name = tensor("q_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1401978624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1403207488))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_197_cast_fp16 = conv(dilations = var_9756, groups = var_6865, pad = q_197_pad_0, pad_type = q_197_pad_type_0, strides = var_9754, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_391_cast_fp16)[name = tensor("q_197_cast_fp16")]; + tensor var_9760 = const()[name = tensor("op_9760"), val = tensor([1, 1])]; + tensor var_9762 = const()[name = tensor("op_9762"), val = tensor([1, 1])]; + tensor k_197_pad_type_0 = const()[name = tensor("k_197_pad_type_0"), val = tensor("custom")]; + tensor k_197_pad_0 = const()[name = tensor("k_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1403207680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1404436544))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_197_cast_fp16 = conv(dilations = var_9762, groups = var_6865, pad = k_197_pad_0, pad_type = k_197_pad_type_0, strides = var_9760, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_391_cast_fp16)[name = tensor("k_197_cast_fp16")]; + tensor var_9766 = const()[name = tensor("op_9766"), val = tensor([1, 1])]; + tensor var_9768 = const()[name = tensor("op_9768"), val = tensor([1, 1])]; + tensor v_197_pad_type_0 = const()[name = tensor("v_197_pad_type_0"), val = tensor("custom")]; + tensor v_197_pad_0 = const()[name = tensor("v_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1404436736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1405665600))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_197_cast_fp16 = conv(dilations = var_9768, groups = var_6865, pad = v_197_pad_0, pad_type = v_197_pad_type_0, strides = var_9766, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_391_cast_fp16)[name = tensor("v_197_cast_fp16")]; + tensor var_9772 = const()[name = tensor("op_9772"), val = tensor([1, 20, 64, -1])]; + tensor var_9773_cast_fp16 = reshape(shape = var_9772, x = q_197_cast_fp16)[name = tensor("op_9773_cast_fp16")]; + tensor var_9774 = const()[name = tensor("op_9774"), val = tensor([1, 20, 64, -1])]; + tensor var_9775_cast_fp16 = reshape(shape = var_9774, x = k_197_cast_fp16)[name = tensor("op_9775_cast_fp16")]; + tensor var_9776 = const()[name = tensor("op_9776"), val = tensor([1, 20, 64, -1])]; + tensor var_9777_cast_fp16 = reshape(shape = var_9776, x = v_197_cast_fp16)[name = tensor("op_9777_cast_fp16")]; + tensor attn_weights_393_transpose_x_0 = const()[name = tensor("attn_weights_393_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_393_transpose_y_0 = const()[name = tensor("attn_weights_393_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_9773_cast_fp16, y = var_9775_cast_fp16)[name = tensor("attn_weights_393_cast_fp16")]; + tensor attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_395_cast_fp16")]; + tensor var_9781_cast_fp16 = softmax(axis = var_6849, x = attn_weights_395_cast_fp16)[name = tensor("op_9781_cast_fp16")]; + tensor attn_197_transpose_x_0 = const()[name = tensor("attn_197_transpose_x_0"), val = tensor(false)]; + tensor attn_197_transpose_y_0 = const()[name = tensor("attn_197_transpose_y_0"), val = tensor(true)]; + tensor attn_197_cast_fp16 = matmul(transpose_x = attn_197_transpose_x_0, transpose_y = attn_197_transpose_y_0, x = var_9777_cast_fp16, y = var_9781_cast_fp16)[name = tensor("attn_197_cast_fp16")]; + tensor var_9785 = const()[name = tensor("op_9785"), val = tensor([1, 1280, 1, -1])]; + tensor input_577_cast_fp16 = reshape(shape = var_9785, x = attn_197_cast_fp16)[name = tensor("input_577_cast_fp16")]; + tensor var_9790 = const()[name = tensor("op_9790"), val = tensor([1, 1])]; + tensor var_9792 = const()[name = tensor("op_9792"), val = tensor([1, 1])]; + tensor var_9794_pad_type_0 = const()[name = tensor("op_9794_pad_type_0"), val = tensor("custom")]; + tensor var_9794_pad_0 = const()[name = tensor("op_9794_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1405665792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406894656))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406894848)))]; + tensor var_9794_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_9792, groups = var_6865, pad = var_9794_pad_0, pad_type = var_9794_pad_type_0, strides = var_9790, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_577_cast_fp16)[name = tensor("op_9794_cast_fp16")]; + tensor inputs_297_cast_fp16 = add(x = var_9794_cast_fp16, y = inputs_295_cast_fp16)[name = tensor("inputs_297_cast_fp16")]; + tensor hidden_states_393_axes_0 = const()[name = tensor("hidden_states_393_axes_0"), val = tensor([1])]; + tensor hidden_states_393_gamma_0_to_fp16 = const()[name = tensor("hidden_states_393_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406897472)))]; + tensor hidden_states_393_beta_0_to_fp16 = const()[name = tensor("hidden_states_393_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406900096)))]; + tensor var_9804_to_fp16 = const()[name = tensor("op_9804_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_393_cast_fp16 = layer_norm(axes = hidden_states_393_axes_0, beta = hidden_states_393_beta_0_to_fp16, epsilon = var_9804_to_fp16, gamma = hidden_states_393_gamma_0_to_fp16, x = inputs_297_cast_fp16)[name = tensor("hidden_states_393_cast_fp16")]; + tensor var_9819 = const()[name = tensor("op_9819"), val = tensor([1, 1])]; + tensor var_9821 = const()[name = tensor("op_9821"), val = tensor([1, 1])]; + tensor q_199_pad_type_0 = const()[name = tensor("q_199_pad_type_0"), val = tensor("custom")]; + tensor q_199_pad_0 = const()[name = tensor("q_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406902720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1408131584))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_199_cast_fp16 = conv(dilations = var_9821, groups = var_6865, pad = q_199_pad_0, pad_type = q_199_pad_type_0, strides = var_9819, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_393_cast_fp16)[name = tensor("q_199_cast_fp16")]; + tensor var_9825 = const()[name = tensor("op_9825"), val = tensor([1, 1])]; + tensor var_9827 = const()[name = tensor("op_9827"), val = tensor([1, 1])]; + tensor k_199_pad_type_0 = const()[name = tensor("k_199_pad_type_0"), val = tensor("custom")]; + tensor k_199_pad_0 = const()[name = tensor("k_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1408131776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1410097920))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_199_cast_fp16 = conv(dilations = var_9827, groups = var_6865, pad = k_199_pad_0, pad_type = k_199_pad_type_0, strides = var_9825, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_199_cast_fp16")]; + tensor var_9831 = const()[name = tensor("op_9831"), val = tensor([1, 1])]; + tensor var_9833 = const()[name = tensor("op_9833"), val = tensor([1, 1])]; + tensor v_199_pad_type_0 = const()[name = tensor("v_199_pad_type_0"), val = tensor("custom")]; + tensor v_199_pad_0 = const()[name = tensor("v_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1410098112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1412064256))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_199_cast_fp16 = conv(dilations = var_9833, groups = var_6865, pad = v_199_pad_0, pad_type = v_199_pad_type_0, strides = var_9831, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_199_cast_fp16")]; + tensor var_9837 = const()[name = tensor("op_9837"), val = tensor([1, 20, 64, -1])]; + tensor var_9838_cast_fp16 = reshape(shape = var_9837, x = q_199_cast_fp16)[name = tensor("op_9838_cast_fp16")]; + tensor var_9839 = const()[name = tensor("op_9839"), val = tensor([1, 20, 64, -1])]; + tensor var_9840_cast_fp16 = reshape(shape = var_9839, x = k_199_cast_fp16)[name = tensor("op_9840_cast_fp16")]; + tensor var_9841 = const()[name = tensor("op_9841"), val = tensor([1, 20, 64, -1])]; + tensor var_9842_cast_fp16 = reshape(shape = var_9841, x = v_199_cast_fp16)[name = tensor("op_9842_cast_fp16")]; + tensor attn_weights_397_transpose_x_0 = const()[name = tensor("attn_weights_397_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_397_transpose_y_0 = const()[name = tensor("attn_weights_397_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_397_cast_fp16 = matmul(transpose_x = attn_weights_397_transpose_x_0, transpose_y = attn_weights_397_transpose_y_0, x = var_9838_cast_fp16, y = var_9840_cast_fp16)[name = tensor("attn_weights_397_cast_fp16")]; + tensor attn_weights_399_cast_fp16 = mul(x = attn_weights_397_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_399_cast_fp16")]; + tensor var_9846_cast_fp16 = softmax(axis = var_6849, x = attn_weights_399_cast_fp16)[name = tensor("op_9846_cast_fp16")]; + tensor attn_199_transpose_x_0 = const()[name = tensor("attn_199_transpose_x_0"), val = tensor(false)]; + tensor attn_199_transpose_y_0 = const()[name = tensor("attn_199_transpose_y_0"), val = tensor(true)]; + tensor attn_199_cast_fp16 = matmul(transpose_x = attn_199_transpose_x_0, transpose_y = attn_199_transpose_y_0, x = var_9842_cast_fp16, y = var_9846_cast_fp16)[name = tensor("attn_199_cast_fp16")]; + tensor var_9850 = const()[name = tensor("op_9850"), val = tensor([1, 1280, 1, -1])]; + tensor input_579_cast_fp16 = reshape(shape = var_9850, x = attn_199_cast_fp16)[name = tensor("input_579_cast_fp16")]; + tensor var_9855 = const()[name = tensor("op_9855"), val = tensor([1, 1])]; + tensor var_9857 = const()[name = tensor("op_9857"), val = tensor([1, 1])]; + tensor var_9859_pad_type_0 = const()[name = tensor("op_9859_pad_type_0"), val = tensor("custom")]; + tensor var_9859_pad_0 = const()[name = tensor("op_9859_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1412064448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413293312))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413293504)))]; + tensor var_9859_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_9857, groups = var_6865, pad = var_9859_pad_0, pad_type = var_9859_pad_type_0, strides = var_9855, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_579_cast_fp16)[name = tensor("op_9859_cast_fp16")]; + tensor inputs_299_cast_fp16 = add(x = var_9859_cast_fp16, y = inputs_297_cast_fp16)[name = tensor("inputs_299_cast_fp16")]; + tensor input_581_axes_0 = const()[name = tensor("input_581_axes_0"), val = tensor([1])]; + tensor input_581_gamma_0_to_fp16 = const()[name = tensor("input_581_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413296128)))]; + tensor input_581_beta_0_to_fp16 = const()[name = tensor("input_581_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413298752)))]; + tensor var_9869_to_fp16 = const()[name = tensor("op_9869_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_581_cast_fp16 = layer_norm(axes = input_581_axes_0, beta = input_581_beta_0_to_fp16, epsilon = var_9869_to_fp16, gamma = input_581_gamma_0_to_fp16, x = inputs_299_cast_fp16)[name = tensor("input_581_cast_fp16")]; + tensor var_9885 = const()[name = tensor("op_9885"), val = tensor([1, 1])]; + tensor var_9887 = const()[name = tensor("op_9887"), val = tensor([1, 1])]; + tensor var_9889_pad_type_0 = const()[name = tensor("op_9889_pad_type_0"), val = tensor("custom")]; + tensor var_9889_pad_0 = const()[name = tensor("op_9889_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413301376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423131840))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423132032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423139776))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_9889_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_9887, groups = var_6865, pad = var_9889_pad_0, pad_type = var_9889_pad_type_0, strides = var_9885, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_581_cast_fp16)[name = tensor("op_9889_cast_fp16")]; + tensor var_9890_split_sizes_0 = const()[name = tensor("op_9890_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9890_axis_0 = const()[name = tensor("op_9890_axis_0"), val = tensor(1)]; + tensor var_9890_cast_fp16_0, tensor var_9890_cast_fp16_1 = split(axis = var_9890_axis_0, split_sizes = var_9890_split_sizes_0, x = var_9889_cast_fp16)[name = tensor("op_9890_cast_fp16")]; + tensor var_9892_mode_0 = const()[name = tensor("op_9892_mode_0"), val = tensor("EXACT")]; + tensor var_9892_cast_fp16 = gelu(mode = var_9892_mode_0, x = var_9890_cast_fp16_1)[name = tensor("op_9892_cast_fp16")]; + tensor input_583_cast_fp16 = mul(x = var_9890_cast_fp16_0, y = var_9892_cast_fp16)[name = tensor("input_583_cast_fp16")]; + tensor var_9896 = const()[name = tensor("op_9896"), val = tensor([1, 1])]; + tensor var_9898 = const()[name = tensor("op_9898"), val = tensor([1, 1])]; + tensor var_9900_pad_type_0 = const()[name = tensor("op_9900_pad_type_0"), val = tensor("custom")]; + tensor var_9900_pad_0 = const()[name = tensor("op_9900_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1423139968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428055232))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428055424)))]; + tensor var_9900_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_9898, groups = var_6865, pad = var_9900_pad_0, pad_type = var_9900_pad_type_0, strides = var_9896, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_583_cast_fp16)[name = tensor("op_9900_cast_fp16")]; + tensor inputs_301_cast_fp16 = add(x = var_9900_cast_fp16, y = inputs_299_cast_fp16)[name = tensor("inputs_301_cast_fp16")]; + tensor hidden_states_397_axes_0 = const()[name = tensor("hidden_states_397_axes_0"), val = tensor([1])]; + tensor hidden_states_397_gamma_0_to_fp16 = const()[name = tensor("hidden_states_397_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428058048)))]; + tensor hidden_states_397_beta_0_to_fp16 = const()[name = tensor("hidden_states_397_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428060672)))]; + tensor var_9916_to_fp16 = const()[name = tensor("op_9916_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_397_cast_fp16 = layer_norm(axes = hidden_states_397_axes_0, beta = hidden_states_397_beta_0_to_fp16, epsilon = var_9916_to_fp16, gamma = hidden_states_397_gamma_0_to_fp16, x = inputs_301_cast_fp16)[name = tensor("hidden_states_397_cast_fp16")]; + tensor var_9931 = const()[name = tensor("op_9931"), val = tensor([1, 1])]; + tensor var_9933 = const()[name = tensor("op_9933"), val = tensor([1, 1])]; + tensor q_201_pad_type_0 = const()[name = tensor("q_201_pad_type_0"), val = tensor("custom")]; + tensor q_201_pad_0 = const()[name = tensor("q_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1428063296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1429292160))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_201_cast_fp16 = conv(dilations = var_9933, groups = var_6865, pad = q_201_pad_0, pad_type = q_201_pad_type_0, strides = var_9931, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_397_cast_fp16)[name = tensor("q_201_cast_fp16")]; + tensor var_9937 = const()[name = tensor("op_9937"), val = tensor([1, 1])]; + tensor var_9939 = const()[name = tensor("op_9939"), val = tensor([1, 1])]; + tensor k_201_pad_type_0 = const()[name = tensor("k_201_pad_type_0"), val = tensor("custom")]; + tensor k_201_pad_0 = const()[name = tensor("k_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1429292352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1430521216))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_201_cast_fp16 = conv(dilations = var_9939, groups = var_6865, pad = k_201_pad_0, pad_type = k_201_pad_type_0, strides = var_9937, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_397_cast_fp16)[name = tensor("k_201_cast_fp16")]; + tensor var_9943 = const()[name = tensor("op_9943"), val = tensor([1, 1])]; + tensor var_9945 = const()[name = tensor("op_9945"), val = tensor([1, 1])]; + tensor v_201_pad_type_0 = const()[name = tensor("v_201_pad_type_0"), val = tensor("custom")]; + tensor v_201_pad_0 = const()[name = tensor("v_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1430521408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1431750272))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_201_cast_fp16 = conv(dilations = var_9945, groups = var_6865, pad = v_201_pad_0, pad_type = v_201_pad_type_0, strides = var_9943, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_397_cast_fp16)[name = tensor("v_201_cast_fp16")]; + tensor var_9949 = const()[name = tensor("op_9949"), val = tensor([1, 20, 64, -1])]; + tensor var_9950_cast_fp16 = reshape(shape = var_9949, x = q_201_cast_fp16)[name = tensor("op_9950_cast_fp16")]; + tensor var_9951 = const()[name = tensor("op_9951"), val = tensor([1, 20, 64, -1])]; + tensor var_9952_cast_fp16 = reshape(shape = var_9951, x = k_201_cast_fp16)[name = tensor("op_9952_cast_fp16")]; + tensor var_9953 = const()[name = tensor("op_9953"), val = tensor([1, 20, 64, -1])]; + tensor var_9954_cast_fp16 = reshape(shape = var_9953, x = v_201_cast_fp16)[name = tensor("op_9954_cast_fp16")]; + tensor attn_weights_401_transpose_x_0 = const()[name = tensor("attn_weights_401_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_401_transpose_y_0 = const()[name = tensor("attn_weights_401_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9950_cast_fp16, y = var_9952_cast_fp16)[name = tensor("attn_weights_401_cast_fp16")]; + tensor attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_403_cast_fp16")]; + tensor var_9958_cast_fp16 = softmax(axis = var_6849, x = attn_weights_403_cast_fp16)[name = tensor("op_9958_cast_fp16")]; + tensor attn_201_transpose_x_0 = const()[name = tensor("attn_201_transpose_x_0"), val = tensor(false)]; + tensor attn_201_transpose_y_0 = const()[name = tensor("attn_201_transpose_y_0"), val = tensor(true)]; + tensor attn_201_cast_fp16 = matmul(transpose_x = attn_201_transpose_x_0, transpose_y = attn_201_transpose_y_0, x = var_9954_cast_fp16, y = var_9958_cast_fp16)[name = tensor("attn_201_cast_fp16")]; + tensor var_9962 = const()[name = tensor("op_9962"), val = tensor([1, 1280, 1, -1])]; + tensor input_585_cast_fp16 = reshape(shape = var_9962, x = attn_201_cast_fp16)[name = tensor("input_585_cast_fp16")]; + tensor var_9967 = const()[name = tensor("op_9967"), val = tensor([1, 1])]; + tensor var_9969 = const()[name = tensor("op_9969"), val = tensor([1, 1])]; + tensor var_9971_pad_type_0 = const()[name = tensor("op_9971_pad_type_0"), val = tensor("custom")]; + tensor var_9971_pad_0 = const()[name = tensor("op_9971_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1431750464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432979328))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432979520)))]; + tensor var_9971_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_9969, groups = var_6865, pad = var_9971_pad_0, pad_type = var_9971_pad_type_0, strides = var_9967, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_585_cast_fp16)[name = tensor("op_9971_cast_fp16")]; + tensor inputs_303_cast_fp16 = add(x = var_9971_cast_fp16, y = inputs_301_cast_fp16)[name = tensor("inputs_303_cast_fp16")]; + tensor hidden_states_399_axes_0 = const()[name = tensor("hidden_states_399_axes_0"), val = tensor([1])]; + tensor hidden_states_399_gamma_0_to_fp16 = const()[name = tensor("hidden_states_399_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432982144)))]; + tensor hidden_states_399_beta_0_to_fp16 = const()[name = tensor("hidden_states_399_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432984768)))]; + tensor var_9981_to_fp16 = const()[name = tensor("op_9981_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_399_cast_fp16 = layer_norm(axes = hidden_states_399_axes_0, beta = hidden_states_399_beta_0_to_fp16, epsilon = var_9981_to_fp16, gamma = hidden_states_399_gamma_0_to_fp16, x = inputs_303_cast_fp16)[name = tensor("hidden_states_399_cast_fp16")]; + tensor var_9996 = const()[name = tensor("op_9996"), val = tensor([1, 1])]; + tensor var_9998 = const()[name = tensor("op_9998"), val = tensor([1, 1])]; + tensor q_203_pad_type_0 = const()[name = tensor("q_203_pad_type_0"), val = tensor("custom")]; + tensor q_203_pad_0 = const()[name = tensor("q_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1432987392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1434216256))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_203_cast_fp16 = conv(dilations = var_9998, groups = var_6865, pad = q_203_pad_0, pad_type = q_203_pad_type_0, strides = var_9996, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_399_cast_fp16)[name = tensor("q_203_cast_fp16")]; + tensor var_10002 = const()[name = tensor("op_10002"), val = tensor([1, 1])]; + tensor var_10004 = const()[name = tensor("op_10004"), val = tensor([1, 1])]; + tensor k_203_pad_type_0 = const()[name = tensor("k_203_pad_type_0"), val = tensor("custom")]; + tensor k_203_pad_0 = const()[name = tensor("k_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1434216448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1436182592))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_203_cast_fp16 = conv(dilations = var_10004, groups = var_6865, pad = k_203_pad_0, pad_type = k_203_pad_type_0, strides = var_10002, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_203_cast_fp16")]; + tensor var_10008 = const()[name = tensor("op_10008"), val = tensor([1, 1])]; + tensor var_10010 = const()[name = tensor("op_10010"), val = tensor([1, 1])]; + tensor v_203_pad_type_0 = const()[name = tensor("v_203_pad_type_0"), val = tensor("custom")]; + tensor v_203_pad_0 = const()[name = tensor("v_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1436182784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1438148928))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_203_cast_fp16 = conv(dilations = var_10010, groups = var_6865, pad = v_203_pad_0, pad_type = v_203_pad_type_0, strides = var_10008, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_203_cast_fp16")]; + tensor var_10014 = const()[name = tensor("op_10014"), val = tensor([1, 20, 64, -1])]; + tensor var_10015_cast_fp16 = reshape(shape = var_10014, x = q_203_cast_fp16)[name = tensor("op_10015_cast_fp16")]; + tensor var_10016 = const()[name = tensor("op_10016"), val = tensor([1, 20, 64, -1])]; + tensor var_10017_cast_fp16 = reshape(shape = var_10016, x = k_203_cast_fp16)[name = tensor("op_10017_cast_fp16")]; + tensor var_10018 = const()[name = tensor("op_10018"), val = tensor([1, 20, 64, -1])]; + tensor var_10019_cast_fp16 = reshape(shape = var_10018, x = v_203_cast_fp16)[name = tensor("op_10019_cast_fp16")]; + tensor attn_weights_405_transpose_x_0 = const()[name = tensor("attn_weights_405_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_405_transpose_y_0 = const()[name = tensor("attn_weights_405_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_405_cast_fp16 = matmul(transpose_x = attn_weights_405_transpose_x_0, transpose_y = attn_weights_405_transpose_y_0, x = var_10015_cast_fp16, y = var_10017_cast_fp16)[name = tensor("attn_weights_405_cast_fp16")]; + tensor attn_weights_407_cast_fp16 = mul(x = attn_weights_405_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_407_cast_fp16")]; + tensor var_10023_cast_fp16 = softmax(axis = var_6849, x = attn_weights_407_cast_fp16)[name = tensor("op_10023_cast_fp16")]; + tensor attn_203_transpose_x_0 = const()[name = tensor("attn_203_transpose_x_0"), val = tensor(false)]; + tensor attn_203_transpose_y_0 = const()[name = tensor("attn_203_transpose_y_0"), val = tensor(true)]; + tensor attn_203_cast_fp16 = matmul(transpose_x = attn_203_transpose_x_0, transpose_y = attn_203_transpose_y_0, x = var_10019_cast_fp16, y = var_10023_cast_fp16)[name = tensor("attn_203_cast_fp16")]; + tensor var_10027 = const()[name = tensor("op_10027"), val = tensor([1, 1280, 1, -1])]; + tensor input_587_cast_fp16 = reshape(shape = var_10027, x = attn_203_cast_fp16)[name = tensor("input_587_cast_fp16")]; + tensor var_10032 = const()[name = tensor("op_10032"), val = tensor([1, 1])]; + tensor var_10034 = const()[name = tensor("op_10034"), val = tensor([1, 1])]; + tensor var_10036_pad_type_0 = const()[name = tensor("op_10036_pad_type_0"), val = tensor("custom")]; + tensor var_10036_pad_0 = const()[name = tensor("op_10036_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1438149120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439377984))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439378176)))]; + tensor var_10036_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_10034, groups = var_6865, pad = var_10036_pad_0, pad_type = var_10036_pad_type_0, strides = var_10032, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_587_cast_fp16)[name = tensor("op_10036_cast_fp16")]; + tensor inputs_305_cast_fp16 = add(x = var_10036_cast_fp16, y = inputs_303_cast_fp16)[name = tensor("inputs_305_cast_fp16")]; + tensor input_589_axes_0 = const()[name = tensor("input_589_axes_0"), val = tensor([1])]; + tensor input_589_gamma_0_to_fp16 = const()[name = tensor("input_589_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439380800)))]; + tensor input_589_beta_0_to_fp16 = const()[name = tensor("input_589_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439383424)))]; + tensor var_10046_to_fp16 = const()[name = tensor("op_10046_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_589_cast_fp16 = layer_norm(axes = input_589_axes_0, beta = input_589_beta_0_to_fp16, epsilon = var_10046_to_fp16, gamma = input_589_gamma_0_to_fp16, x = inputs_305_cast_fp16)[name = tensor("input_589_cast_fp16")]; + tensor var_10062 = const()[name = tensor("op_10062"), val = tensor([1, 1])]; + tensor var_10064 = const()[name = tensor("op_10064"), val = tensor([1, 1])]; + tensor var_10066_pad_type_0 = const()[name = tensor("op_10066_pad_type_0"), val = tensor("custom")]; + tensor var_10066_pad_0 = const()[name = tensor("op_10066_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1439386048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449216512))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449216704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449224448))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10066_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10064, groups = var_6865, pad = var_10066_pad_0, pad_type = var_10066_pad_type_0, strides = var_10062, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_589_cast_fp16)[name = tensor("op_10066_cast_fp16")]; + tensor var_10067_split_sizes_0 = const()[name = tensor("op_10067_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10067_axis_0 = const()[name = tensor("op_10067_axis_0"), val = tensor(1)]; + tensor var_10067_cast_fp16_0, tensor var_10067_cast_fp16_1 = split(axis = var_10067_axis_0, split_sizes = var_10067_split_sizes_0, x = var_10066_cast_fp16)[name = tensor("op_10067_cast_fp16")]; + tensor var_10069_mode_0 = const()[name = tensor("op_10069_mode_0"), val = tensor("EXACT")]; + tensor var_10069_cast_fp16 = gelu(mode = var_10069_mode_0, x = var_10067_cast_fp16_1)[name = tensor("op_10069_cast_fp16")]; + tensor input_591_cast_fp16 = mul(x = var_10067_cast_fp16_0, y = var_10069_cast_fp16)[name = tensor("input_591_cast_fp16")]; + tensor var_10073 = const()[name = tensor("op_10073"), val = tensor([1, 1])]; + tensor var_10075 = const()[name = tensor("op_10075"), val = tensor([1, 1])]; + tensor var_10077_pad_type_0 = const()[name = tensor("op_10077_pad_type_0"), val = tensor("custom")]; + tensor var_10077_pad_0 = const()[name = tensor("op_10077_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1449224640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454139904))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454140096)))]; + tensor var_10077_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_10075, groups = var_6865, pad = var_10077_pad_0, pad_type = var_10077_pad_type_0, strides = var_10073, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_591_cast_fp16)[name = tensor("op_10077_cast_fp16")]; + tensor inputs_307_cast_fp16 = add(x = var_10077_cast_fp16, y = inputs_305_cast_fp16)[name = tensor("inputs_307_cast_fp16")]; + tensor hidden_states_403_axes_0 = const()[name = tensor("hidden_states_403_axes_0"), val = tensor([1])]; + tensor hidden_states_403_gamma_0_to_fp16 = const()[name = tensor("hidden_states_403_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454142720)))]; + tensor hidden_states_403_beta_0_to_fp16 = const()[name = tensor("hidden_states_403_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454145344)))]; + tensor var_10093_to_fp16 = const()[name = tensor("op_10093_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_403_cast_fp16 = layer_norm(axes = hidden_states_403_axes_0, beta = hidden_states_403_beta_0_to_fp16, epsilon = var_10093_to_fp16, gamma = hidden_states_403_gamma_0_to_fp16, x = inputs_307_cast_fp16)[name = tensor("hidden_states_403_cast_fp16")]; + tensor var_10108 = const()[name = tensor("op_10108"), val = tensor([1, 1])]; + tensor var_10110 = const()[name = tensor("op_10110"), val = tensor([1, 1])]; + tensor q_205_pad_type_0 = const()[name = tensor("q_205_pad_type_0"), val = tensor("custom")]; + tensor q_205_pad_0 = const()[name = tensor("q_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454147968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1455376832))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_205_cast_fp16 = conv(dilations = var_10110, groups = var_6865, pad = q_205_pad_0, pad_type = q_205_pad_type_0, strides = var_10108, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_403_cast_fp16)[name = tensor("q_205_cast_fp16")]; + tensor var_10114 = const()[name = tensor("op_10114"), val = tensor([1, 1])]; + tensor var_10116 = const()[name = tensor("op_10116"), val = tensor([1, 1])]; + tensor k_205_pad_type_0 = const()[name = tensor("k_205_pad_type_0"), val = tensor("custom")]; + tensor k_205_pad_0 = const()[name = tensor("k_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1455377024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1456605888))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_205_cast_fp16 = conv(dilations = var_10116, groups = var_6865, pad = k_205_pad_0, pad_type = k_205_pad_type_0, strides = var_10114, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_403_cast_fp16)[name = tensor("k_205_cast_fp16")]; + tensor var_10120 = const()[name = tensor("op_10120"), val = tensor([1, 1])]; + tensor var_10122 = const()[name = tensor("op_10122"), val = tensor([1, 1])]; + tensor v_205_pad_type_0 = const()[name = tensor("v_205_pad_type_0"), val = tensor("custom")]; + tensor v_205_pad_0 = const()[name = tensor("v_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1456606080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1457834944))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_205_cast_fp16 = conv(dilations = var_10122, groups = var_6865, pad = v_205_pad_0, pad_type = v_205_pad_type_0, strides = var_10120, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_403_cast_fp16)[name = tensor("v_205_cast_fp16")]; + tensor var_10126 = const()[name = tensor("op_10126"), val = tensor([1, 20, 64, -1])]; + tensor var_10127_cast_fp16 = reshape(shape = var_10126, x = q_205_cast_fp16)[name = tensor("op_10127_cast_fp16")]; + tensor var_10128 = const()[name = tensor("op_10128"), val = tensor([1, 20, 64, -1])]; + tensor var_10129_cast_fp16 = reshape(shape = var_10128, x = k_205_cast_fp16)[name = tensor("op_10129_cast_fp16")]; + tensor var_10130 = const()[name = tensor("op_10130"), val = tensor([1, 20, 64, -1])]; + tensor var_10131_cast_fp16 = reshape(shape = var_10130, x = v_205_cast_fp16)[name = tensor("op_10131_cast_fp16")]; + tensor attn_weights_409_transpose_x_0 = const()[name = tensor("attn_weights_409_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_409_transpose_y_0 = const()[name = tensor("attn_weights_409_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_10127_cast_fp16, y = var_10129_cast_fp16)[name = tensor("attn_weights_409_cast_fp16")]; + tensor attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_411_cast_fp16")]; + tensor var_10135_cast_fp16 = softmax(axis = var_6849, x = attn_weights_411_cast_fp16)[name = tensor("op_10135_cast_fp16")]; + tensor attn_205_transpose_x_0 = const()[name = tensor("attn_205_transpose_x_0"), val = tensor(false)]; + tensor attn_205_transpose_y_0 = const()[name = tensor("attn_205_transpose_y_0"), val = tensor(true)]; + tensor attn_205_cast_fp16 = matmul(transpose_x = attn_205_transpose_x_0, transpose_y = attn_205_transpose_y_0, x = var_10131_cast_fp16, y = var_10135_cast_fp16)[name = tensor("attn_205_cast_fp16")]; + tensor var_10139 = const()[name = tensor("op_10139"), val = tensor([1, 1280, 1, -1])]; + tensor input_593_cast_fp16 = reshape(shape = var_10139, x = attn_205_cast_fp16)[name = tensor("input_593_cast_fp16")]; + tensor var_10144 = const()[name = tensor("op_10144"), val = tensor([1, 1])]; + tensor var_10146 = const()[name = tensor("op_10146"), val = tensor([1, 1])]; + tensor var_10148_pad_type_0 = const()[name = tensor("op_10148_pad_type_0"), val = tensor("custom")]; + tensor var_10148_pad_0 = const()[name = tensor("op_10148_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1457835136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459064000))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459064192)))]; + tensor var_10148_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_10146, groups = var_6865, pad = var_10148_pad_0, pad_type = var_10148_pad_type_0, strides = var_10144, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_593_cast_fp16)[name = tensor("op_10148_cast_fp16")]; + tensor inputs_309_cast_fp16 = add(x = var_10148_cast_fp16, y = inputs_307_cast_fp16)[name = tensor("inputs_309_cast_fp16")]; + tensor hidden_states_405_axes_0 = const()[name = tensor("hidden_states_405_axes_0"), val = tensor([1])]; + tensor hidden_states_405_gamma_0_to_fp16 = const()[name = tensor("hidden_states_405_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459066816)))]; + tensor hidden_states_405_beta_0_to_fp16 = const()[name = tensor("hidden_states_405_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459069440)))]; + tensor var_10158_to_fp16 = const()[name = tensor("op_10158_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_405_cast_fp16 = layer_norm(axes = hidden_states_405_axes_0, beta = hidden_states_405_beta_0_to_fp16, epsilon = var_10158_to_fp16, gamma = hidden_states_405_gamma_0_to_fp16, x = inputs_309_cast_fp16)[name = tensor("hidden_states_405_cast_fp16")]; + tensor var_10173 = const()[name = tensor("op_10173"), val = tensor([1, 1])]; + tensor var_10175 = const()[name = tensor("op_10175"), val = tensor([1, 1])]; + tensor q_207_pad_type_0 = const()[name = tensor("q_207_pad_type_0"), val = tensor("custom")]; + tensor q_207_pad_0 = const()[name = tensor("q_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1459072064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1460300928))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_207_cast_fp16 = conv(dilations = var_10175, groups = var_6865, pad = q_207_pad_0, pad_type = q_207_pad_type_0, strides = var_10173, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_405_cast_fp16)[name = tensor("q_207_cast_fp16")]; + tensor var_10179 = const()[name = tensor("op_10179"), val = tensor([1, 1])]; + tensor var_10181 = const()[name = tensor("op_10181"), val = tensor([1, 1])]; + tensor k_207_pad_type_0 = const()[name = tensor("k_207_pad_type_0"), val = tensor("custom")]; + tensor k_207_pad_0 = const()[name = tensor("k_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1460301120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1462267264))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_207_cast_fp16 = conv(dilations = var_10181, groups = var_6865, pad = k_207_pad_0, pad_type = k_207_pad_type_0, strides = var_10179, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_207_cast_fp16")]; + tensor var_10185 = const()[name = tensor("op_10185"), val = tensor([1, 1])]; + tensor var_10187 = const()[name = tensor("op_10187"), val = tensor([1, 1])]; + tensor v_207_pad_type_0 = const()[name = tensor("v_207_pad_type_0"), val = tensor("custom")]; + tensor v_207_pad_0 = const()[name = tensor("v_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1462267456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464233600))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_207_cast_fp16 = conv(dilations = var_10187, groups = var_6865, pad = v_207_pad_0, pad_type = v_207_pad_type_0, strides = var_10185, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_207_cast_fp16")]; + tensor var_10191 = const()[name = tensor("op_10191"), val = tensor([1, 20, 64, -1])]; + tensor var_10192_cast_fp16 = reshape(shape = var_10191, x = q_207_cast_fp16)[name = tensor("op_10192_cast_fp16")]; + tensor var_10193 = const()[name = tensor("op_10193"), val = tensor([1, 20, 64, -1])]; + tensor var_10194_cast_fp16 = reshape(shape = var_10193, x = k_207_cast_fp16)[name = tensor("op_10194_cast_fp16")]; + tensor var_10195 = const()[name = tensor("op_10195"), val = tensor([1, 20, 64, -1])]; + tensor var_10196_cast_fp16 = reshape(shape = var_10195, x = v_207_cast_fp16)[name = tensor("op_10196_cast_fp16")]; + tensor attn_weights_413_transpose_x_0 = const()[name = tensor("attn_weights_413_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_413_transpose_y_0 = const()[name = tensor("attn_weights_413_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_413_cast_fp16 = matmul(transpose_x = attn_weights_413_transpose_x_0, transpose_y = attn_weights_413_transpose_y_0, x = var_10192_cast_fp16, y = var_10194_cast_fp16)[name = tensor("attn_weights_413_cast_fp16")]; + tensor attn_weights_415_cast_fp16 = mul(x = attn_weights_413_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_415_cast_fp16")]; + tensor var_10200_cast_fp16 = softmax(axis = var_6849, x = attn_weights_415_cast_fp16)[name = tensor("op_10200_cast_fp16")]; + tensor attn_207_transpose_x_0 = const()[name = tensor("attn_207_transpose_x_0"), val = tensor(false)]; + tensor attn_207_transpose_y_0 = const()[name = tensor("attn_207_transpose_y_0"), val = tensor(true)]; + tensor attn_207_cast_fp16 = matmul(transpose_x = attn_207_transpose_x_0, transpose_y = attn_207_transpose_y_0, x = var_10196_cast_fp16, y = var_10200_cast_fp16)[name = tensor("attn_207_cast_fp16")]; + tensor var_10204 = const()[name = tensor("op_10204"), val = tensor([1, 1280, 1, -1])]; + tensor input_595_cast_fp16 = reshape(shape = var_10204, x = attn_207_cast_fp16)[name = tensor("input_595_cast_fp16")]; + tensor var_10209 = const()[name = tensor("op_10209"), val = tensor([1, 1])]; + tensor var_10211 = const()[name = tensor("op_10211"), val = tensor([1, 1])]; + tensor var_10213_pad_type_0 = const()[name = tensor("op_10213_pad_type_0"), val = tensor("custom")]; + tensor var_10213_pad_0 = const()[name = tensor("op_10213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464233792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465462656))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465462848)))]; + tensor var_10213_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_10211, groups = var_6865, pad = var_10213_pad_0, pad_type = var_10213_pad_type_0, strides = var_10209, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_595_cast_fp16)[name = tensor("op_10213_cast_fp16")]; + tensor inputs_311_cast_fp16 = add(x = var_10213_cast_fp16, y = inputs_309_cast_fp16)[name = tensor("inputs_311_cast_fp16")]; + tensor input_597_axes_0 = const()[name = tensor("input_597_axes_0"), val = tensor([1])]; + tensor input_597_gamma_0_to_fp16 = const()[name = tensor("input_597_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465465472)))]; + tensor input_597_beta_0_to_fp16 = const()[name = tensor("input_597_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465468096)))]; + tensor var_10223_to_fp16 = const()[name = tensor("op_10223_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_597_cast_fp16 = layer_norm(axes = input_597_axes_0, beta = input_597_beta_0_to_fp16, epsilon = var_10223_to_fp16, gamma = input_597_gamma_0_to_fp16, x = inputs_311_cast_fp16)[name = tensor("input_597_cast_fp16")]; + tensor var_10239 = const()[name = tensor("op_10239"), val = tensor([1, 1])]; + tensor var_10241 = const()[name = tensor("op_10241"), val = tensor([1, 1])]; + tensor var_10243_pad_type_0 = const()[name = tensor("op_10243_pad_type_0"), val = tensor("custom")]; + tensor var_10243_pad_0 = const()[name = tensor("op_10243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465470720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475301184))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475301376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475309120))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10243_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10241, groups = var_6865, pad = var_10243_pad_0, pad_type = var_10243_pad_type_0, strides = var_10239, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_597_cast_fp16)[name = tensor("op_10243_cast_fp16")]; + tensor var_10244_split_sizes_0 = const()[name = tensor("op_10244_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10244_axis_0 = const()[name = tensor("op_10244_axis_0"), val = tensor(1)]; + tensor var_10244_cast_fp16_0, tensor var_10244_cast_fp16_1 = split(axis = var_10244_axis_0, split_sizes = var_10244_split_sizes_0, x = var_10243_cast_fp16)[name = tensor("op_10244_cast_fp16")]; + tensor var_10246_mode_0 = const()[name = tensor("op_10246_mode_0"), val = tensor("EXACT")]; + tensor var_10246_cast_fp16 = gelu(mode = var_10246_mode_0, x = var_10244_cast_fp16_1)[name = tensor("op_10246_cast_fp16")]; + tensor input_599_cast_fp16 = mul(x = var_10244_cast_fp16_0, y = var_10246_cast_fp16)[name = tensor("input_599_cast_fp16")]; + tensor var_10250 = const()[name = tensor("op_10250"), val = tensor([1, 1])]; + tensor var_10252 = const()[name = tensor("op_10252"), val = tensor([1, 1])]; + tensor var_10254_pad_type_0 = const()[name = tensor("op_10254_pad_type_0"), val = tensor("custom")]; + tensor var_10254_pad_0 = const()[name = tensor("op_10254_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475309312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480224576))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480224768)))]; + tensor var_10254_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_10252, groups = var_6865, pad = var_10254_pad_0, pad_type = var_10254_pad_type_0, strides = var_10250, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_599_cast_fp16)[name = tensor("op_10254_cast_fp16")]; + tensor inputs_313_cast_fp16 = add(x = var_10254_cast_fp16, y = inputs_311_cast_fp16)[name = tensor("inputs_313_cast_fp16")]; + tensor hidden_states_409_axes_0 = const()[name = tensor("hidden_states_409_axes_0"), val = tensor([1])]; + tensor hidden_states_409_gamma_0_to_fp16 = const()[name = tensor("hidden_states_409_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480227392)))]; + tensor hidden_states_409_beta_0_to_fp16 = const()[name = tensor("hidden_states_409_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480230016)))]; + tensor var_10270_to_fp16 = const()[name = tensor("op_10270_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_409_cast_fp16 = layer_norm(axes = hidden_states_409_axes_0, beta = hidden_states_409_beta_0_to_fp16, epsilon = var_10270_to_fp16, gamma = hidden_states_409_gamma_0_to_fp16, x = inputs_313_cast_fp16)[name = tensor("hidden_states_409_cast_fp16")]; + tensor var_10285 = const()[name = tensor("op_10285"), val = tensor([1, 1])]; + tensor var_10287 = const()[name = tensor("op_10287"), val = tensor([1, 1])]; + tensor q_209_pad_type_0 = const()[name = tensor("q_209_pad_type_0"), val = tensor("custom")]; + tensor q_209_pad_0 = const()[name = tensor("q_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1480232640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1481461504))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_209_cast_fp16 = conv(dilations = var_10287, groups = var_6865, pad = q_209_pad_0, pad_type = q_209_pad_type_0, strides = var_10285, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_409_cast_fp16)[name = tensor("q_209_cast_fp16")]; + tensor var_10291 = const()[name = tensor("op_10291"), val = tensor([1, 1])]; + tensor var_10293 = const()[name = tensor("op_10293"), val = tensor([1, 1])]; + tensor k_209_pad_type_0 = const()[name = tensor("k_209_pad_type_0"), val = tensor("custom")]; + tensor k_209_pad_0 = const()[name = tensor("k_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1481461696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1482690560))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_209_cast_fp16 = conv(dilations = var_10293, groups = var_6865, pad = k_209_pad_0, pad_type = k_209_pad_type_0, strides = var_10291, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_409_cast_fp16)[name = tensor("k_209_cast_fp16")]; + tensor var_10297 = const()[name = tensor("op_10297"), val = tensor([1, 1])]; + tensor var_10299 = const()[name = tensor("op_10299"), val = tensor([1, 1])]; + tensor v_209_pad_type_0 = const()[name = tensor("v_209_pad_type_0"), val = tensor("custom")]; + tensor v_209_pad_0 = const()[name = tensor("v_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1482690752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1483919616))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_209_cast_fp16 = conv(dilations = var_10299, groups = var_6865, pad = v_209_pad_0, pad_type = v_209_pad_type_0, strides = var_10297, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_409_cast_fp16)[name = tensor("v_209_cast_fp16")]; + tensor var_10303 = const()[name = tensor("op_10303"), val = tensor([1, 20, 64, -1])]; + tensor var_10304_cast_fp16 = reshape(shape = var_10303, x = q_209_cast_fp16)[name = tensor("op_10304_cast_fp16")]; + tensor var_10305 = const()[name = tensor("op_10305"), val = tensor([1, 20, 64, -1])]; + tensor var_10306_cast_fp16 = reshape(shape = var_10305, x = k_209_cast_fp16)[name = tensor("op_10306_cast_fp16")]; + tensor var_10307 = const()[name = tensor("op_10307"), val = tensor([1, 20, 64, -1])]; + tensor var_10308_cast_fp16 = reshape(shape = var_10307, x = v_209_cast_fp16)[name = tensor("op_10308_cast_fp16")]; + tensor attn_weights_417_transpose_x_0 = const()[name = tensor("attn_weights_417_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_417_transpose_y_0 = const()[name = tensor("attn_weights_417_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_10304_cast_fp16, y = var_10306_cast_fp16)[name = tensor("attn_weights_417_cast_fp16")]; + tensor attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_419_cast_fp16")]; + tensor var_10312_cast_fp16 = softmax(axis = var_6849, x = attn_weights_419_cast_fp16)[name = tensor("op_10312_cast_fp16")]; + tensor attn_209_transpose_x_0 = const()[name = tensor("attn_209_transpose_x_0"), val = tensor(false)]; + tensor attn_209_transpose_y_0 = const()[name = tensor("attn_209_transpose_y_0"), val = tensor(true)]; + tensor attn_209_cast_fp16 = matmul(transpose_x = attn_209_transpose_x_0, transpose_y = attn_209_transpose_y_0, x = var_10308_cast_fp16, y = var_10312_cast_fp16)[name = tensor("attn_209_cast_fp16")]; + tensor var_10316 = const()[name = tensor("op_10316"), val = tensor([1, 1280, 1, -1])]; + tensor input_601_cast_fp16 = reshape(shape = var_10316, x = attn_209_cast_fp16)[name = tensor("input_601_cast_fp16")]; + tensor var_10321 = const()[name = tensor("op_10321"), val = tensor([1, 1])]; + tensor var_10323 = const()[name = tensor("op_10323"), val = tensor([1, 1])]; + tensor var_10325_pad_type_0 = const()[name = tensor("op_10325_pad_type_0"), val = tensor("custom")]; + tensor var_10325_pad_0 = const()[name = tensor("op_10325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1483919808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485148672))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485148864)))]; + tensor var_10325_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_10323, groups = var_6865, pad = var_10325_pad_0, pad_type = var_10325_pad_type_0, strides = var_10321, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_601_cast_fp16)[name = tensor("op_10325_cast_fp16")]; + tensor inputs_315_cast_fp16 = add(x = var_10325_cast_fp16, y = inputs_313_cast_fp16)[name = tensor("inputs_315_cast_fp16")]; + tensor hidden_states_411_axes_0 = const()[name = tensor("hidden_states_411_axes_0"), val = tensor([1])]; + tensor hidden_states_411_gamma_0_to_fp16 = const()[name = tensor("hidden_states_411_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485151488)))]; + tensor hidden_states_411_beta_0_to_fp16 = const()[name = tensor("hidden_states_411_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485154112)))]; + tensor var_10335_to_fp16 = const()[name = tensor("op_10335_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_411_cast_fp16 = layer_norm(axes = hidden_states_411_axes_0, beta = hidden_states_411_beta_0_to_fp16, epsilon = var_10335_to_fp16, gamma = hidden_states_411_gamma_0_to_fp16, x = inputs_315_cast_fp16)[name = tensor("hidden_states_411_cast_fp16")]; + tensor var_10350 = const()[name = tensor("op_10350"), val = tensor([1, 1])]; + tensor var_10352 = const()[name = tensor("op_10352"), val = tensor([1, 1])]; + tensor q_211_pad_type_0 = const()[name = tensor("q_211_pad_type_0"), val = tensor("custom")]; + tensor q_211_pad_0 = const()[name = tensor("q_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1485156736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486385600))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_211_cast_fp16 = conv(dilations = var_10352, groups = var_6865, pad = q_211_pad_0, pad_type = q_211_pad_type_0, strides = var_10350, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_411_cast_fp16)[name = tensor("q_211_cast_fp16")]; + tensor var_10356 = const()[name = tensor("op_10356"), val = tensor([1, 1])]; + tensor var_10358 = const()[name = tensor("op_10358"), val = tensor([1, 1])]; + tensor k_211_pad_type_0 = const()[name = tensor("k_211_pad_type_0"), val = tensor("custom")]; + tensor k_211_pad_0 = const()[name = tensor("k_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486385792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1488351936))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_211_cast_fp16 = conv(dilations = var_10358, groups = var_6865, pad = k_211_pad_0, pad_type = k_211_pad_type_0, strides = var_10356, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_211_cast_fp16")]; + tensor var_10362 = const()[name = tensor("op_10362"), val = tensor([1, 1])]; + tensor var_10364 = const()[name = tensor("op_10364"), val = tensor([1, 1])]; + tensor v_211_pad_type_0 = const()[name = tensor("v_211_pad_type_0"), val = tensor("custom")]; + tensor v_211_pad_0 = const()[name = tensor("v_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1488352128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1490318272))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_211_cast_fp16 = conv(dilations = var_10364, groups = var_6865, pad = v_211_pad_0, pad_type = v_211_pad_type_0, strides = var_10362, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_211_cast_fp16")]; + tensor var_10368 = const()[name = tensor("op_10368"), val = tensor([1, 20, 64, -1])]; + tensor var_10369_cast_fp16 = reshape(shape = var_10368, x = q_211_cast_fp16)[name = tensor("op_10369_cast_fp16")]; + tensor var_10370 = const()[name = tensor("op_10370"), val = tensor([1, 20, 64, -1])]; + tensor var_10371_cast_fp16 = reshape(shape = var_10370, x = k_211_cast_fp16)[name = tensor("op_10371_cast_fp16")]; + tensor var_10372 = const()[name = tensor("op_10372"), val = tensor([1, 20, 64, -1])]; + tensor var_10373_cast_fp16 = reshape(shape = var_10372, x = v_211_cast_fp16)[name = tensor("op_10373_cast_fp16")]; + tensor attn_weights_421_transpose_x_0 = const()[name = tensor("attn_weights_421_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_421_transpose_y_0 = const()[name = tensor("attn_weights_421_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_421_cast_fp16 = matmul(transpose_x = attn_weights_421_transpose_x_0, transpose_y = attn_weights_421_transpose_y_0, x = var_10369_cast_fp16, y = var_10371_cast_fp16)[name = tensor("attn_weights_421_cast_fp16")]; + tensor attn_weights_423_cast_fp16 = mul(x = attn_weights_421_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_423_cast_fp16")]; + tensor var_10377_cast_fp16 = softmax(axis = var_6849, x = attn_weights_423_cast_fp16)[name = tensor("op_10377_cast_fp16")]; + tensor attn_211_transpose_x_0 = const()[name = tensor("attn_211_transpose_x_0"), val = tensor(false)]; + tensor attn_211_transpose_y_0 = const()[name = tensor("attn_211_transpose_y_0"), val = tensor(true)]; + tensor attn_211_cast_fp16 = matmul(transpose_x = attn_211_transpose_x_0, transpose_y = attn_211_transpose_y_0, x = var_10373_cast_fp16, y = var_10377_cast_fp16)[name = tensor("attn_211_cast_fp16")]; + tensor var_10381 = const()[name = tensor("op_10381"), val = tensor([1, 1280, 1, -1])]; + tensor input_603_cast_fp16 = reshape(shape = var_10381, x = attn_211_cast_fp16)[name = tensor("input_603_cast_fp16")]; + tensor var_10386 = const()[name = tensor("op_10386"), val = tensor([1, 1])]; + tensor var_10388 = const()[name = tensor("op_10388"), val = tensor([1, 1])]; + tensor var_10390_pad_type_0 = const()[name = tensor("op_10390_pad_type_0"), val = tensor("custom")]; + tensor var_10390_pad_0 = const()[name = tensor("op_10390_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1490318464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491547328))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491547520)))]; + tensor var_10390_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_10388, groups = var_6865, pad = var_10390_pad_0, pad_type = var_10390_pad_type_0, strides = var_10386, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_603_cast_fp16)[name = tensor("op_10390_cast_fp16")]; + tensor inputs_317_cast_fp16 = add(x = var_10390_cast_fp16, y = inputs_315_cast_fp16)[name = tensor("inputs_317_cast_fp16")]; + tensor input_605_axes_0 = const()[name = tensor("input_605_axes_0"), val = tensor([1])]; + tensor input_605_gamma_0_to_fp16 = const()[name = tensor("input_605_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491550144)))]; + tensor input_605_beta_0_to_fp16 = const()[name = tensor("input_605_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491552768)))]; + tensor var_10400_to_fp16 = const()[name = tensor("op_10400_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_605_cast_fp16 = layer_norm(axes = input_605_axes_0, beta = input_605_beta_0_to_fp16, epsilon = var_10400_to_fp16, gamma = input_605_gamma_0_to_fp16, x = inputs_317_cast_fp16)[name = tensor("input_605_cast_fp16")]; + tensor var_10416 = const()[name = tensor("op_10416"), val = tensor([1, 1])]; + tensor var_10418 = const()[name = tensor("op_10418"), val = tensor([1, 1])]; + tensor var_10420_pad_type_0 = const()[name = tensor("op_10420_pad_type_0"), val = tensor("custom")]; + tensor var_10420_pad_0 = const()[name = tensor("op_10420_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1491555392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501385856))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501386048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501393792))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10420_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10418, groups = var_6865, pad = var_10420_pad_0, pad_type = var_10420_pad_type_0, strides = var_10416, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_605_cast_fp16)[name = tensor("op_10420_cast_fp16")]; + tensor var_10421_split_sizes_0 = const()[name = tensor("op_10421_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10421_axis_0 = const()[name = tensor("op_10421_axis_0"), val = tensor(1)]; + tensor var_10421_cast_fp16_0, tensor var_10421_cast_fp16_1 = split(axis = var_10421_axis_0, split_sizes = var_10421_split_sizes_0, x = var_10420_cast_fp16)[name = tensor("op_10421_cast_fp16")]; + tensor var_10423_mode_0 = const()[name = tensor("op_10423_mode_0"), val = tensor("EXACT")]; + tensor var_10423_cast_fp16 = gelu(mode = var_10423_mode_0, x = var_10421_cast_fp16_1)[name = tensor("op_10423_cast_fp16")]; + tensor input_607_cast_fp16 = mul(x = var_10421_cast_fp16_0, y = var_10423_cast_fp16)[name = tensor("input_607_cast_fp16")]; + tensor var_10427 = const()[name = tensor("op_10427"), val = tensor([1, 1])]; + tensor var_10429 = const()[name = tensor("op_10429"), val = tensor([1, 1])]; + tensor var_10431_pad_type_0 = const()[name = tensor("op_10431_pad_type_0"), val = tensor("custom")]; + tensor var_10431_pad_0 = const()[name = tensor("op_10431_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1501393984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506309248))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506309440)))]; + tensor var_10431_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_10429, groups = var_6865, pad = var_10431_pad_0, pad_type = var_10431_pad_type_0, strides = var_10427, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_607_cast_fp16)[name = tensor("op_10431_cast_fp16")]; + tensor inputs_319_cast_fp16 = add(x = var_10431_cast_fp16, y = inputs_317_cast_fp16)[name = tensor("inputs_319_cast_fp16")]; + tensor hidden_states_415_axes_0 = const()[name = tensor("hidden_states_415_axes_0"), val = tensor([1])]; + tensor hidden_states_415_gamma_0_to_fp16 = const()[name = tensor("hidden_states_415_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506312064)))]; + tensor hidden_states_415_beta_0_to_fp16 = const()[name = tensor("hidden_states_415_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506314688)))]; + tensor var_10447_to_fp16 = const()[name = tensor("op_10447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_415_cast_fp16 = layer_norm(axes = hidden_states_415_axes_0, beta = hidden_states_415_beta_0_to_fp16, epsilon = var_10447_to_fp16, gamma = hidden_states_415_gamma_0_to_fp16, x = inputs_319_cast_fp16)[name = tensor("hidden_states_415_cast_fp16")]; + tensor var_10462 = const()[name = tensor("op_10462"), val = tensor([1, 1])]; + tensor var_10464 = const()[name = tensor("op_10464"), val = tensor([1, 1])]; + tensor q_213_pad_type_0 = const()[name = tensor("q_213_pad_type_0"), val = tensor("custom")]; + tensor q_213_pad_0 = const()[name = tensor("q_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1506317312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1507546176))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_213_cast_fp16 = conv(dilations = var_10464, groups = var_6865, pad = q_213_pad_0, pad_type = q_213_pad_type_0, strides = var_10462, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_415_cast_fp16)[name = tensor("q_213_cast_fp16")]; + tensor var_10468 = const()[name = tensor("op_10468"), val = tensor([1, 1])]; + tensor var_10470 = const()[name = tensor("op_10470"), val = tensor([1, 1])]; + tensor k_213_pad_type_0 = const()[name = tensor("k_213_pad_type_0"), val = tensor("custom")]; + tensor k_213_pad_0 = const()[name = tensor("k_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1507546368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1508775232))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_213_cast_fp16 = conv(dilations = var_10470, groups = var_6865, pad = k_213_pad_0, pad_type = k_213_pad_type_0, strides = var_10468, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_415_cast_fp16)[name = tensor("k_213_cast_fp16")]; + tensor var_10474 = const()[name = tensor("op_10474"), val = tensor([1, 1])]; + tensor var_10476 = const()[name = tensor("op_10476"), val = tensor([1, 1])]; + tensor v_213_pad_type_0 = const()[name = tensor("v_213_pad_type_0"), val = tensor("custom")]; + tensor v_213_pad_0 = const()[name = tensor("v_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1508775424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1510004288))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_213_cast_fp16 = conv(dilations = var_10476, groups = var_6865, pad = v_213_pad_0, pad_type = v_213_pad_type_0, strides = var_10474, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_415_cast_fp16)[name = tensor("v_213_cast_fp16")]; + tensor var_10480 = const()[name = tensor("op_10480"), val = tensor([1, 20, 64, -1])]; + tensor var_10481_cast_fp16 = reshape(shape = var_10480, x = q_213_cast_fp16)[name = tensor("op_10481_cast_fp16")]; + tensor var_10482 = const()[name = tensor("op_10482"), val = tensor([1, 20, 64, -1])]; + tensor var_10483_cast_fp16 = reshape(shape = var_10482, x = k_213_cast_fp16)[name = tensor("op_10483_cast_fp16")]; + tensor var_10484 = const()[name = tensor("op_10484"), val = tensor([1, 20, 64, -1])]; + tensor var_10485_cast_fp16 = reshape(shape = var_10484, x = v_213_cast_fp16)[name = tensor("op_10485_cast_fp16")]; + tensor attn_weights_425_transpose_x_0 = const()[name = tensor("attn_weights_425_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_425_transpose_y_0 = const()[name = tensor("attn_weights_425_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_10481_cast_fp16, y = var_10483_cast_fp16)[name = tensor("attn_weights_425_cast_fp16")]; + tensor attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_427_cast_fp16")]; + tensor var_10489_cast_fp16 = softmax(axis = var_6849, x = attn_weights_427_cast_fp16)[name = tensor("op_10489_cast_fp16")]; + tensor attn_213_transpose_x_0 = const()[name = tensor("attn_213_transpose_x_0"), val = tensor(false)]; + tensor attn_213_transpose_y_0 = const()[name = tensor("attn_213_transpose_y_0"), val = tensor(true)]; + tensor attn_213_cast_fp16 = matmul(transpose_x = attn_213_transpose_x_0, transpose_y = attn_213_transpose_y_0, x = var_10485_cast_fp16, y = var_10489_cast_fp16)[name = tensor("attn_213_cast_fp16")]; + tensor var_10493 = const()[name = tensor("op_10493"), val = tensor([1, 1280, 1, -1])]; + tensor input_609_cast_fp16 = reshape(shape = var_10493, x = attn_213_cast_fp16)[name = tensor("input_609_cast_fp16")]; + tensor var_10498 = const()[name = tensor("op_10498"), val = tensor([1, 1])]; + tensor var_10500 = const()[name = tensor("op_10500"), val = tensor([1, 1])]; + tensor var_10502_pad_type_0 = const()[name = tensor("op_10502_pad_type_0"), val = tensor("custom")]; + tensor var_10502_pad_0 = const()[name = tensor("op_10502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1510004480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511233344))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511233536)))]; + tensor var_10502_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_10500, groups = var_6865, pad = var_10502_pad_0, pad_type = var_10502_pad_type_0, strides = var_10498, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_609_cast_fp16)[name = tensor("op_10502_cast_fp16")]; + tensor inputs_321_cast_fp16 = add(x = var_10502_cast_fp16, y = inputs_319_cast_fp16)[name = tensor("inputs_321_cast_fp16")]; + tensor hidden_states_417_axes_0 = const()[name = tensor("hidden_states_417_axes_0"), val = tensor([1])]; + tensor hidden_states_417_gamma_0_to_fp16 = const()[name = tensor("hidden_states_417_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511236160)))]; + tensor hidden_states_417_beta_0_to_fp16 = const()[name = tensor("hidden_states_417_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511238784)))]; + tensor var_10512_to_fp16 = const()[name = tensor("op_10512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_417_cast_fp16 = layer_norm(axes = hidden_states_417_axes_0, beta = hidden_states_417_beta_0_to_fp16, epsilon = var_10512_to_fp16, gamma = hidden_states_417_gamma_0_to_fp16, x = inputs_321_cast_fp16)[name = tensor("hidden_states_417_cast_fp16")]; + tensor var_10527 = const()[name = tensor("op_10527"), val = tensor([1, 1])]; + tensor var_10529 = const()[name = tensor("op_10529"), val = tensor([1, 1])]; + tensor q_215_pad_type_0 = const()[name = tensor("q_215_pad_type_0"), val = tensor("custom")]; + tensor q_215_pad_0 = const()[name = tensor("q_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1511241408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1512470272))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_215_cast_fp16 = conv(dilations = var_10529, groups = var_6865, pad = q_215_pad_0, pad_type = q_215_pad_type_0, strides = var_10527, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_417_cast_fp16)[name = tensor("q_215_cast_fp16")]; + tensor var_10533 = const()[name = tensor("op_10533"), val = tensor([1, 1])]; + tensor var_10535 = const()[name = tensor("op_10535"), val = tensor([1, 1])]; + tensor k_215_pad_type_0 = const()[name = tensor("k_215_pad_type_0"), val = tensor("custom")]; + tensor k_215_pad_0 = const()[name = tensor("k_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1512470464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1514436608))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_215_cast_fp16 = conv(dilations = var_10535, groups = var_6865, pad = k_215_pad_0, pad_type = k_215_pad_type_0, strides = var_10533, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_215_cast_fp16")]; + tensor var_10539 = const()[name = tensor("op_10539"), val = tensor([1, 1])]; + tensor var_10541 = const()[name = tensor("op_10541"), val = tensor([1, 1])]; + tensor v_215_pad_type_0 = const()[name = tensor("v_215_pad_type_0"), val = tensor("custom")]; + tensor v_215_pad_0 = const()[name = tensor("v_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1514436800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1516402944))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_215_cast_fp16 = conv(dilations = var_10541, groups = var_6865, pad = v_215_pad_0, pad_type = v_215_pad_type_0, strides = var_10539, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_215_cast_fp16")]; + tensor var_10545 = const()[name = tensor("op_10545"), val = tensor([1, 20, 64, -1])]; + tensor var_10546_cast_fp16 = reshape(shape = var_10545, x = q_215_cast_fp16)[name = tensor("op_10546_cast_fp16")]; + tensor var_10547 = const()[name = tensor("op_10547"), val = tensor([1, 20, 64, -1])]; + tensor var_10548_cast_fp16 = reshape(shape = var_10547, x = k_215_cast_fp16)[name = tensor("op_10548_cast_fp16")]; + tensor var_10549 = const()[name = tensor("op_10549"), val = tensor([1, 20, 64, -1])]; + tensor var_10550_cast_fp16 = reshape(shape = var_10549, x = v_215_cast_fp16)[name = tensor("op_10550_cast_fp16")]; + tensor attn_weights_429_transpose_x_0 = const()[name = tensor("attn_weights_429_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_429_transpose_y_0 = const()[name = tensor("attn_weights_429_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_429_cast_fp16 = matmul(transpose_x = attn_weights_429_transpose_x_0, transpose_y = attn_weights_429_transpose_y_0, x = var_10546_cast_fp16, y = var_10548_cast_fp16)[name = tensor("attn_weights_429_cast_fp16")]; + tensor attn_weights_431_cast_fp16 = mul(x = attn_weights_429_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_431_cast_fp16")]; + tensor var_10554_cast_fp16 = softmax(axis = var_6849, x = attn_weights_431_cast_fp16)[name = tensor("op_10554_cast_fp16")]; + tensor attn_215_transpose_x_0 = const()[name = tensor("attn_215_transpose_x_0"), val = tensor(false)]; + tensor attn_215_transpose_y_0 = const()[name = tensor("attn_215_transpose_y_0"), val = tensor(true)]; + tensor attn_215_cast_fp16 = matmul(transpose_x = attn_215_transpose_x_0, transpose_y = attn_215_transpose_y_0, x = var_10550_cast_fp16, y = var_10554_cast_fp16)[name = tensor("attn_215_cast_fp16")]; + tensor var_10558 = const()[name = tensor("op_10558"), val = tensor([1, 1280, 1, -1])]; + tensor input_611_cast_fp16 = reshape(shape = var_10558, x = attn_215_cast_fp16)[name = tensor("input_611_cast_fp16")]; + tensor var_10563 = const()[name = tensor("op_10563"), val = tensor([1, 1])]; + tensor var_10565 = const()[name = tensor("op_10565"), val = tensor([1, 1])]; + tensor var_10567_pad_type_0 = const()[name = tensor("op_10567_pad_type_0"), val = tensor("custom")]; + tensor var_10567_pad_0 = const()[name = tensor("op_10567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1516403136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517632000))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517632192)))]; + tensor var_10567_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_10565, groups = var_6865, pad = var_10567_pad_0, pad_type = var_10567_pad_type_0, strides = var_10563, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = tensor("op_10567_cast_fp16")]; + tensor inputs_323_cast_fp16 = add(x = var_10567_cast_fp16, y = inputs_321_cast_fp16)[name = tensor("inputs_323_cast_fp16")]; + tensor input_613_axes_0 = const()[name = tensor("input_613_axes_0"), val = tensor([1])]; + tensor input_613_gamma_0_to_fp16 = const()[name = tensor("input_613_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517634816)))]; + tensor input_613_beta_0_to_fp16 = const()[name = tensor("input_613_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517637440)))]; + tensor var_10577_to_fp16 = const()[name = tensor("op_10577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_613_cast_fp16 = layer_norm(axes = input_613_axes_0, beta = input_613_beta_0_to_fp16, epsilon = var_10577_to_fp16, gamma = input_613_gamma_0_to_fp16, x = inputs_323_cast_fp16)[name = tensor("input_613_cast_fp16")]; + tensor var_10593 = const()[name = tensor("op_10593"), val = tensor([1, 1])]; + tensor var_10595 = const()[name = tensor("op_10595"), val = tensor([1, 1])]; + tensor var_10597_pad_type_0 = const()[name = tensor("op_10597_pad_type_0"), val = tensor("custom")]; + tensor var_10597_pad_0 = const()[name = tensor("op_10597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1517640064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527470528))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527470720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527478464))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10597_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10595, groups = var_6865, pad = var_10597_pad_0, pad_type = var_10597_pad_type_0, strides = var_10593, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_613_cast_fp16)[name = tensor("op_10597_cast_fp16")]; + tensor var_10598_split_sizes_0 = const()[name = tensor("op_10598_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10598_axis_0 = const()[name = tensor("op_10598_axis_0"), val = tensor(1)]; + tensor var_10598_cast_fp16_0, tensor var_10598_cast_fp16_1 = split(axis = var_10598_axis_0, split_sizes = var_10598_split_sizes_0, x = var_10597_cast_fp16)[name = tensor("op_10598_cast_fp16")]; + tensor var_10600_mode_0 = const()[name = tensor("op_10600_mode_0"), val = tensor("EXACT")]; + tensor var_10600_cast_fp16 = gelu(mode = var_10600_mode_0, x = var_10598_cast_fp16_1)[name = tensor("op_10600_cast_fp16")]; + tensor input_615_cast_fp16 = mul(x = var_10598_cast_fp16_0, y = var_10600_cast_fp16)[name = tensor("input_615_cast_fp16")]; + tensor var_10604 = const()[name = tensor("op_10604"), val = tensor([1, 1])]; + tensor var_10606 = const()[name = tensor("op_10606"), val = tensor([1, 1])]; + tensor var_10608_pad_type_0 = const()[name = tensor("op_10608_pad_type_0"), val = tensor("custom")]; + tensor var_10608_pad_0 = const()[name = tensor("op_10608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1527478656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532393920))), name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532394112)))]; + tensor var_10608_cast_fp16 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_10606, groups = var_6865, pad = var_10608_pad_0, pad_type = var_10608_pad_type_0, strides = var_10604, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = tensor("op_10608_cast_fp16")]; + tensor hidden_states_421_cast_fp16 = add(x = var_10608_cast_fp16, y = inputs_323_cast_fp16)[name = tensor("hidden_states_421_cast_fp16")]; + tensor var_10610 = const()[name = tensor("op_10610"), val = tensor([1, 1280, 32, 32])]; + tensor input_617_cast_fp16 = reshape(shape = var_10610, x = hidden_states_421_cast_fp16)[name = tensor("input_617_cast_fp16")]; + tensor var_10614 = const()[name = tensor("op_10614"), val = tensor([1, 1])]; + tensor var_10616 = const()[name = tensor("op_10616"), val = tensor([1, 1])]; + tensor hidden_states_423_pad_type_0 = const()[name = tensor("hidden_states_423_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_423_pad_0 = const()[name = tensor("hidden_states_423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532396736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533625600))), name = tensor("up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533625792)))]; + tensor hidden_states_423_cast_fp16 = conv(bias = up_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_10616, groups = var_6865, pad = hidden_states_423_pad_0, pad_type = hidden_states_423_pad_type_0, strides = var_10614, weight = up_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_617_cast_fp16)[name = tensor("hidden_states_423_cast_fp16")]; + tensor hidden_states_425_cast_fp16 = add(x = hidden_states_423_cast_fp16, y = hidden_states_357_cast_fp16)[name = tensor("hidden_states_425_cast_fp16")]; + tensor input_619_interleave_0 = const()[name = tensor("input_619_interleave_0"), val = tensor(false)]; + tensor input_619_cast_fp16 = concat(axis = var_6865, interleave = input_619_interleave_0, values = (hidden_states_425_cast_fp16, input_115_cast_fp16))[name = tensor("input_619_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 60, 32, 32])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = input_619_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 1920, 32, 32])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533628416)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533632320)))]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533636224)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533640128)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor input_623_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("input_623_cast_fp16")]; + tensor var_10634 = const()[name = tensor("op_10634"), val = tensor([1, 1])]; + tensor var_10636 = const()[name = tensor("op_10636"), val = tensor([1, 1])]; + tensor hidden_states_427_pad_type_0 = const()[name = tensor("hidden_states_427_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_427_pad_0 = const()[name = tensor("hidden_states_427_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1533644032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550232896))), name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 3, 3])]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550233088)))]; + tensor hidden_states_427_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_10636, groups = var_6865, pad = hidden_states_427_pad_0, pad_type = hidden_states_427_pad_type_0, strides = var_10634, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_623_cast_fp16)[name = tensor("hidden_states_427_cast_fp16")]; + tensor var_10642 = const()[name = tensor("op_10642"), val = tensor([1, 1])]; + tensor var_10644 = const()[name = tensor("op_10644"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550235712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551464576))), name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551464768)))]; + tensor temb_21_cast_fp16 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_10644, groups = var_6865, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_10642, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_21_cast_fp16")]; + tensor input_627_cast_fp16 = add(x = hidden_states_427_cast_fp16, y = temb_21_cast_fp16)[name = tensor("input_627_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_627_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551467392)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551470016)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_631_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_631_cast_fp16")]; + tensor var_10654 = const()[name = tensor("op_10654"), val = tensor([1, 1])]; + tensor var_10656 = const()[name = tensor("op_10656"), val = tensor([1, 1])]; + tensor hidden_states_429_pad_type_0 = const()[name = tensor("hidden_states_429_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_429_pad_0 = const()[name = tensor("hidden_states_429_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551472640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1562531904))), name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1562532096)))]; + tensor hidden_states_429_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_10656, groups = var_6865, pad = hidden_states_429_pad_0, pad_type = hidden_states_429_pad_type_0, strides = var_10654, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_631_cast_fp16)[name = tensor("hidden_states_429_cast_fp16")]; + tensor var_10661 = const()[name = tensor("op_10661"), val = tensor([1, 1])]; + tensor var_10663 = const()[name = tensor("op_10663"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1562534720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564377984))), name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 1, 1])]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564378176)))]; + tensor x_9_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_10663, groups = var_6865, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_10661, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_619_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor hidden_states_431_cast_fp16 = add(x = x_9_cast_fp16, y = hidden_states_429_cast_fp16)[name = tensor("hidden_states_431_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 40, 32, 32])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = hidden_states_431_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 1280, 32, 32])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564380800)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564383424)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor var_10701 = const()[name = tensor("op_10701"), val = tensor([1, 1])]; + tensor var_10703 = const()[name = tensor("op_10703"), val = tensor([1, 1])]; + tensor hidden_states_433_pad_type_0 = const()[name = tensor("hidden_states_433_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_433_pad_0 = const()[name = tensor("hidden_states_433_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1564386048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565614912))), name = tensor("up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565615104)))]; + tensor hidden_states_433_cast_fp16 = conv(bias = up_blocks_0_attentions_2_proj_in_bias_to_fp16, dilations = var_10703, groups = var_6865, pad = hidden_states_433_pad_0, pad_type = hidden_states_433_pad_type_0, strides = var_10701, weight = up_blocks_0_attentions_2_proj_in_weight_to_fp16_palettized, x = add_59_cast_fp16)[name = tensor("hidden_states_433_cast_fp16")]; + tensor var_10708 = const()[name = tensor("op_10708"), val = tensor([1, 1280, 1, 1024])]; + tensor inputs_325_cast_fp16 = reshape(shape = var_10708, x = hidden_states_433_cast_fp16)[name = tensor("inputs_325_cast_fp16")]; + tensor hidden_states_435_axes_0 = const()[name = tensor("hidden_states_435_axes_0"), val = tensor([1])]; + tensor hidden_states_435_gamma_0_to_fp16 = const()[name = tensor("hidden_states_435_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565617728)))]; + tensor hidden_states_435_beta_0_to_fp16 = const()[name = tensor("hidden_states_435_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565620352)))]; + tensor var_10724_to_fp16 = const()[name = tensor("op_10724_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_435_cast_fp16 = layer_norm(axes = hidden_states_435_axes_0, beta = hidden_states_435_beta_0_to_fp16, epsilon = var_10724_to_fp16, gamma = hidden_states_435_gamma_0_to_fp16, x = inputs_325_cast_fp16)[name = tensor("hidden_states_435_cast_fp16")]; + tensor var_10739 = const()[name = tensor("op_10739"), val = tensor([1, 1])]; + tensor var_10741 = const()[name = tensor("op_10741"), val = tensor([1, 1])]; + tensor q_217_pad_type_0 = const()[name = tensor("q_217_pad_type_0"), val = tensor("custom")]; + tensor q_217_pad_0 = const()[name = tensor("q_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1565622976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1566851840))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_217_cast_fp16 = conv(dilations = var_10741, groups = var_6865, pad = q_217_pad_0, pad_type = q_217_pad_type_0, strides = var_10739, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_435_cast_fp16)[name = tensor("q_217_cast_fp16")]; + tensor var_10745 = const()[name = tensor("op_10745"), val = tensor([1, 1])]; + tensor var_10747 = const()[name = tensor("op_10747"), val = tensor([1, 1])]; + tensor k_217_pad_type_0 = const()[name = tensor("k_217_pad_type_0"), val = tensor("custom")]; + tensor k_217_pad_0 = const()[name = tensor("k_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1566852032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568080896))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_217_cast_fp16 = conv(dilations = var_10747, groups = var_6865, pad = k_217_pad_0, pad_type = k_217_pad_type_0, strides = var_10745, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_435_cast_fp16)[name = tensor("k_217_cast_fp16")]; + tensor var_10751 = const()[name = tensor("op_10751"), val = tensor([1, 1])]; + tensor var_10753 = const()[name = tensor("op_10753"), val = tensor([1, 1])]; + tensor v_217_pad_type_0 = const()[name = tensor("v_217_pad_type_0"), val = tensor("custom")]; + tensor v_217_pad_0 = const()[name = tensor("v_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1568081088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1569309952))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_217_cast_fp16 = conv(dilations = var_10753, groups = var_6865, pad = v_217_pad_0, pad_type = v_217_pad_type_0, strides = var_10751, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_435_cast_fp16)[name = tensor("v_217_cast_fp16")]; + tensor var_10757 = const()[name = tensor("op_10757"), val = tensor([1, 20, 64, -1])]; + tensor var_10758_cast_fp16 = reshape(shape = var_10757, x = q_217_cast_fp16)[name = tensor("op_10758_cast_fp16")]; + tensor var_10759 = const()[name = tensor("op_10759"), val = tensor([1, 20, 64, -1])]; + tensor var_10760_cast_fp16 = reshape(shape = var_10759, x = k_217_cast_fp16)[name = tensor("op_10760_cast_fp16")]; + tensor var_10761 = const()[name = tensor("op_10761"), val = tensor([1, 20, 64, -1])]; + tensor var_10762_cast_fp16 = reshape(shape = var_10761, x = v_217_cast_fp16)[name = tensor("op_10762_cast_fp16")]; + tensor attn_weights_433_transpose_x_0 = const()[name = tensor("attn_weights_433_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_433_transpose_y_0 = const()[name = tensor("attn_weights_433_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_10758_cast_fp16, y = var_10760_cast_fp16)[name = tensor("attn_weights_433_cast_fp16")]; + tensor attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_435_cast_fp16")]; + tensor var_10766_cast_fp16 = softmax(axis = var_6849, x = attn_weights_435_cast_fp16)[name = tensor("op_10766_cast_fp16")]; + tensor attn_217_transpose_x_0 = const()[name = tensor("attn_217_transpose_x_0"), val = tensor(false)]; + tensor attn_217_transpose_y_0 = const()[name = tensor("attn_217_transpose_y_0"), val = tensor(true)]; + tensor attn_217_cast_fp16 = matmul(transpose_x = attn_217_transpose_x_0, transpose_y = attn_217_transpose_y_0, x = var_10762_cast_fp16, y = var_10766_cast_fp16)[name = tensor("attn_217_cast_fp16")]; + tensor var_10770 = const()[name = tensor("op_10770"), val = tensor([1, 1280, 1, -1])]; + tensor input_635_cast_fp16 = reshape(shape = var_10770, x = attn_217_cast_fp16)[name = tensor("input_635_cast_fp16")]; + tensor var_10775 = const()[name = tensor("op_10775"), val = tensor([1, 1])]; + tensor var_10777 = const()[name = tensor("op_10777"), val = tensor([1, 1])]; + tensor var_10779_pad_type_0 = const()[name = tensor("op_10779_pad_type_0"), val = tensor("custom")]; + tensor var_10779_pad_0 = const()[name = tensor("op_10779_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1569310144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570539008))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570539200)))]; + tensor var_10779_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_10777, groups = var_6865, pad = var_10779_pad_0, pad_type = var_10779_pad_type_0, strides = var_10775, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_635_cast_fp16)[name = tensor("op_10779_cast_fp16")]; + tensor inputs_327_cast_fp16 = add(x = var_10779_cast_fp16, y = inputs_325_cast_fp16)[name = tensor("inputs_327_cast_fp16")]; + tensor hidden_states_437_axes_0 = const()[name = tensor("hidden_states_437_axes_0"), val = tensor([1])]; + tensor hidden_states_437_gamma_0_to_fp16 = const()[name = tensor("hidden_states_437_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570541824)))]; + tensor hidden_states_437_beta_0_to_fp16 = const()[name = tensor("hidden_states_437_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570544448)))]; + tensor var_10789_to_fp16 = const()[name = tensor("op_10789_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_437_cast_fp16 = layer_norm(axes = hidden_states_437_axes_0, beta = hidden_states_437_beta_0_to_fp16, epsilon = var_10789_to_fp16, gamma = hidden_states_437_gamma_0_to_fp16, x = inputs_327_cast_fp16)[name = tensor("hidden_states_437_cast_fp16")]; + tensor var_10804 = const()[name = tensor("op_10804"), val = tensor([1, 1])]; + tensor var_10806 = const()[name = tensor("op_10806"), val = tensor([1, 1])]; + tensor q_219_pad_type_0 = const()[name = tensor("q_219_pad_type_0"), val = tensor("custom")]; + tensor q_219_pad_0 = const()[name = tensor("q_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570547072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1571775936))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_219_cast_fp16 = conv(dilations = var_10806, groups = var_6865, pad = q_219_pad_0, pad_type = q_219_pad_type_0, strides = var_10804, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_437_cast_fp16)[name = tensor("q_219_cast_fp16")]; + tensor var_10810 = const()[name = tensor("op_10810"), val = tensor([1, 1])]; + tensor var_10812 = const()[name = tensor("op_10812"), val = tensor([1, 1])]; + tensor k_219_pad_type_0 = const()[name = tensor("k_219_pad_type_0"), val = tensor("custom")]; + tensor k_219_pad_0 = const()[name = tensor("k_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1571776128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1573742272))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_219_cast_fp16 = conv(dilations = var_10812, groups = var_6865, pad = k_219_pad_0, pad_type = k_219_pad_type_0, strides = var_10810, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_219_cast_fp16")]; + tensor var_10816 = const()[name = tensor("op_10816"), val = tensor([1, 1])]; + tensor var_10818 = const()[name = tensor("op_10818"), val = tensor([1, 1])]; + tensor v_219_pad_type_0 = const()[name = tensor("v_219_pad_type_0"), val = tensor("custom")]; + tensor v_219_pad_0 = const()[name = tensor("v_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1573742464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1575708608))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_219_cast_fp16 = conv(dilations = var_10818, groups = var_6865, pad = v_219_pad_0, pad_type = v_219_pad_type_0, strides = var_10816, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_219_cast_fp16")]; + tensor var_10822 = const()[name = tensor("op_10822"), val = tensor([1, 20, 64, -1])]; + tensor var_10823_cast_fp16 = reshape(shape = var_10822, x = q_219_cast_fp16)[name = tensor("op_10823_cast_fp16")]; + tensor var_10824 = const()[name = tensor("op_10824"), val = tensor([1, 20, 64, -1])]; + tensor var_10825_cast_fp16 = reshape(shape = var_10824, x = k_219_cast_fp16)[name = tensor("op_10825_cast_fp16")]; + tensor var_10826 = const()[name = tensor("op_10826"), val = tensor([1, 20, 64, -1])]; + tensor var_10827_cast_fp16 = reshape(shape = var_10826, x = v_219_cast_fp16)[name = tensor("op_10827_cast_fp16")]; + tensor attn_weights_437_transpose_x_0 = const()[name = tensor("attn_weights_437_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_437_transpose_y_0 = const()[name = tensor("attn_weights_437_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_437_cast_fp16 = matmul(transpose_x = attn_weights_437_transpose_x_0, transpose_y = attn_weights_437_transpose_y_0, x = var_10823_cast_fp16, y = var_10825_cast_fp16)[name = tensor("attn_weights_437_cast_fp16")]; + tensor attn_weights_439_cast_fp16 = mul(x = attn_weights_437_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_439_cast_fp16")]; + tensor var_10831_cast_fp16 = softmax(axis = var_6849, x = attn_weights_439_cast_fp16)[name = tensor("op_10831_cast_fp16")]; + tensor attn_219_transpose_x_0 = const()[name = tensor("attn_219_transpose_x_0"), val = tensor(false)]; + tensor attn_219_transpose_y_0 = const()[name = tensor("attn_219_transpose_y_0"), val = tensor(true)]; + tensor attn_219_cast_fp16 = matmul(transpose_x = attn_219_transpose_x_0, transpose_y = attn_219_transpose_y_0, x = var_10827_cast_fp16, y = var_10831_cast_fp16)[name = tensor("attn_219_cast_fp16")]; + tensor var_10835 = const()[name = tensor("op_10835"), val = tensor([1, 1280, 1, -1])]; + tensor input_637_cast_fp16 = reshape(shape = var_10835, x = attn_219_cast_fp16)[name = tensor("input_637_cast_fp16")]; + tensor var_10840 = const()[name = tensor("op_10840"), val = tensor([1, 1])]; + tensor var_10842 = const()[name = tensor("op_10842"), val = tensor([1, 1])]; + tensor var_10844_pad_type_0 = const()[name = tensor("op_10844_pad_type_0"), val = tensor("custom")]; + tensor var_10844_pad_0 = const()[name = tensor("op_10844_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1575708800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576937664))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576937856)))]; + tensor var_10844_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_10842, groups = var_6865, pad = var_10844_pad_0, pad_type = var_10844_pad_type_0, strides = var_10840, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = tensor("op_10844_cast_fp16")]; + tensor inputs_329_cast_fp16 = add(x = var_10844_cast_fp16, y = inputs_327_cast_fp16)[name = tensor("inputs_329_cast_fp16")]; + tensor input_639_axes_0 = const()[name = tensor("input_639_axes_0"), val = tensor([1])]; + tensor input_639_gamma_0_to_fp16 = const()[name = tensor("input_639_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576940480)))]; + tensor input_639_beta_0_to_fp16 = const()[name = tensor("input_639_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576943104)))]; + tensor var_10854_to_fp16 = const()[name = tensor("op_10854_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = input_639_beta_0_to_fp16, epsilon = var_10854_to_fp16, gamma = input_639_gamma_0_to_fp16, x = inputs_329_cast_fp16)[name = tensor("input_639_cast_fp16")]; + tensor var_10870 = const()[name = tensor("op_10870"), val = tensor([1, 1])]; + tensor var_10872 = const()[name = tensor("op_10872"), val = tensor([1, 1])]; + tensor var_10874_pad_type_0 = const()[name = tensor("op_10874_pad_type_0"), val = tensor("custom")]; + tensor var_10874_pad_0 = const()[name = tensor("op_10874_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1576945728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586776192))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586776384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586784128))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_10874_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_10872, groups = var_6865, pad = var_10874_pad_0, pad_type = var_10874_pad_type_0, strides = var_10870, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_639_cast_fp16)[name = tensor("op_10874_cast_fp16")]; + tensor var_10875_split_sizes_0 = const()[name = tensor("op_10875_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10875_axis_0 = const()[name = tensor("op_10875_axis_0"), val = tensor(1)]; + tensor var_10875_cast_fp16_0, tensor var_10875_cast_fp16_1 = split(axis = var_10875_axis_0, split_sizes = var_10875_split_sizes_0, x = var_10874_cast_fp16)[name = tensor("op_10875_cast_fp16")]; + tensor var_10877_mode_0 = const()[name = tensor("op_10877_mode_0"), val = tensor("EXACT")]; + tensor var_10877_cast_fp16 = gelu(mode = var_10877_mode_0, x = var_10875_cast_fp16_1)[name = tensor("op_10877_cast_fp16")]; + tensor input_641_cast_fp16 = mul(x = var_10875_cast_fp16_0, y = var_10877_cast_fp16)[name = tensor("input_641_cast_fp16")]; + tensor var_10881 = const()[name = tensor("op_10881"), val = tensor([1, 1])]; + tensor var_10883 = const()[name = tensor("op_10883"), val = tensor([1, 1])]; + tensor var_10885_pad_type_0 = const()[name = tensor("op_10885_pad_type_0"), val = tensor("custom")]; + tensor var_10885_pad_0 = const()[name = tensor("op_10885_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586784320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591699584))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591699776)))]; + tensor var_10885_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_10883, groups = var_6865, pad = var_10885_pad_0, pad_type = var_10885_pad_type_0, strides = var_10881, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_641_cast_fp16)[name = tensor("op_10885_cast_fp16")]; + tensor inputs_331_cast_fp16 = add(x = var_10885_cast_fp16, y = inputs_329_cast_fp16)[name = tensor("inputs_331_cast_fp16")]; + tensor hidden_states_441_axes_0 = const()[name = tensor("hidden_states_441_axes_0"), val = tensor([1])]; + tensor hidden_states_441_gamma_0_to_fp16 = const()[name = tensor("hidden_states_441_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591702400)))]; + tensor hidden_states_441_beta_0_to_fp16 = const()[name = tensor("hidden_states_441_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591705024)))]; + tensor var_10901_to_fp16 = const()[name = tensor("op_10901_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_441_cast_fp16 = layer_norm(axes = hidden_states_441_axes_0, beta = hidden_states_441_beta_0_to_fp16, epsilon = var_10901_to_fp16, gamma = hidden_states_441_gamma_0_to_fp16, x = inputs_331_cast_fp16)[name = tensor("hidden_states_441_cast_fp16")]; + tensor var_10916 = const()[name = tensor("op_10916"), val = tensor([1, 1])]; + tensor var_10918 = const()[name = tensor("op_10918"), val = tensor([1, 1])]; + tensor q_221_pad_type_0 = const()[name = tensor("q_221_pad_type_0"), val = tensor("custom")]; + tensor q_221_pad_0 = const()[name = tensor("q_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591707648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1592936512))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_221_cast_fp16 = conv(dilations = var_10918, groups = var_6865, pad = q_221_pad_0, pad_type = q_221_pad_type_0, strides = var_10916, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_441_cast_fp16)[name = tensor("q_221_cast_fp16")]; + tensor var_10922 = const()[name = tensor("op_10922"), val = tensor([1, 1])]; + tensor var_10924 = const()[name = tensor("op_10924"), val = tensor([1, 1])]; + tensor k_221_pad_type_0 = const()[name = tensor("k_221_pad_type_0"), val = tensor("custom")]; + tensor k_221_pad_0 = const()[name = tensor("k_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1592936704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1594165568))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_221_cast_fp16 = conv(dilations = var_10924, groups = var_6865, pad = k_221_pad_0, pad_type = k_221_pad_type_0, strides = var_10922, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_441_cast_fp16)[name = tensor("k_221_cast_fp16")]; + tensor var_10928 = const()[name = tensor("op_10928"), val = tensor([1, 1])]; + tensor var_10930 = const()[name = tensor("op_10930"), val = tensor([1, 1])]; + tensor v_221_pad_type_0 = const()[name = tensor("v_221_pad_type_0"), val = tensor("custom")]; + tensor v_221_pad_0 = const()[name = tensor("v_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1594165760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1595394624))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_221_cast_fp16 = conv(dilations = var_10930, groups = var_6865, pad = v_221_pad_0, pad_type = v_221_pad_type_0, strides = var_10928, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_441_cast_fp16)[name = tensor("v_221_cast_fp16")]; + tensor var_10934 = const()[name = tensor("op_10934"), val = tensor([1, 20, 64, -1])]; + tensor var_10935_cast_fp16 = reshape(shape = var_10934, x = q_221_cast_fp16)[name = tensor("op_10935_cast_fp16")]; + tensor var_10936 = const()[name = tensor("op_10936"), val = tensor([1, 20, 64, -1])]; + tensor var_10937_cast_fp16 = reshape(shape = var_10936, x = k_221_cast_fp16)[name = tensor("op_10937_cast_fp16")]; + tensor var_10938 = const()[name = tensor("op_10938"), val = tensor([1, 20, 64, -1])]; + tensor var_10939_cast_fp16 = reshape(shape = var_10938, x = v_221_cast_fp16)[name = tensor("op_10939_cast_fp16")]; + tensor attn_weights_441_transpose_x_0 = const()[name = tensor("attn_weights_441_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_441_transpose_y_0 = const()[name = tensor("attn_weights_441_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_10935_cast_fp16, y = var_10937_cast_fp16)[name = tensor("attn_weights_441_cast_fp16")]; + tensor attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_443_cast_fp16")]; + tensor var_10943_cast_fp16 = softmax(axis = var_6849, x = attn_weights_443_cast_fp16)[name = tensor("op_10943_cast_fp16")]; + tensor attn_221_transpose_x_0 = const()[name = tensor("attn_221_transpose_x_0"), val = tensor(false)]; + tensor attn_221_transpose_y_0 = const()[name = tensor("attn_221_transpose_y_0"), val = tensor(true)]; + tensor attn_221_cast_fp16 = matmul(transpose_x = attn_221_transpose_x_0, transpose_y = attn_221_transpose_y_0, x = var_10939_cast_fp16, y = var_10943_cast_fp16)[name = tensor("attn_221_cast_fp16")]; + tensor var_10947 = const()[name = tensor("op_10947"), val = tensor([1, 1280, 1, -1])]; + tensor input_643_cast_fp16 = reshape(shape = var_10947, x = attn_221_cast_fp16)[name = tensor("input_643_cast_fp16")]; + tensor var_10952 = const()[name = tensor("op_10952"), val = tensor([1, 1])]; + tensor var_10954 = const()[name = tensor("op_10954"), val = tensor([1, 1])]; + tensor var_10956_pad_type_0 = const()[name = tensor("op_10956_pad_type_0"), val = tensor("custom")]; + tensor var_10956_pad_0 = const()[name = tensor("op_10956_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1595394816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596623680))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596623872)))]; + tensor var_10956_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_10954, groups = var_6865, pad = var_10956_pad_0, pad_type = var_10956_pad_type_0, strides = var_10952, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_643_cast_fp16)[name = tensor("op_10956_cast_fp16")]; + tensor inputs_333_cast_fp16 = add(x = var_10956_cast_fp16, y = inputs_331_cast_fp16)[name = tensor("inputs_333_cast_fp16")]; + tensor hidden_states_443_axes_0 = const()[name = tensor("hidden_states_443_axes_0"), val = tensor([1])]; + tensor hidden_states_443_gamma_0_to_fp16 = const()[name = tensor("hidden_states_443_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596626496)))]; + tensor hidden_states_443_beta_0_to_fp16 = const()[name = tensor("hidden_states_443_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596629120)))]; + tensor var_10966_to_fp16 = const()[name = tensor("op_10966_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_443_cast_fp16 = layer_norm(axes = hidden_states_443_axes_0, beta = hidden_states_443_beta_0_to_fp16, epsilon = var_10966_to_fp16, gamma = hidden_states_443_gamma_0_to_fp16, x = inputs_333_cast_fp16)[name = tensor("hidden_states_443_cast_fp16")]; + tensor var_10981 = const()[name = tensor("op_10981"), val = tensor([1, 1])]; + tensor var_10983 = const()[name = tensor("op_10983"), val = tensor([1, 1])]; + tensor q_223_pad_type_0 = const()[name = tensor("q_223_pad_type_0"), val = tensor("custom")]; + tensor q_223_pad_0 = const()[name = tensor("q_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596631744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1597860608))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_223_cast_fp16 = conv(dilations = var_10983, groups = var_6865, pad = q_223_pad_0, pad_type = q_223_pad_type_0, strides = var_10981, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_443_cast_fp16)[name = tensor("q_223_cast_fp16")]; + tensor var_10987 = const()[name = tensor("op_10987"), val = tensor([1, 1])]; + tensor var_10989 = const()[name = tensor("op_10989"), val = tensor([1, 1])]; + tensor k_223_pad_type_0 = const()[name = tensor("k_223_pad_type_0"), val = tensor("custom")]; + tensor k_223_pad_0 = const()[name = tensor("k_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1597860800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1599826944))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_223_cast_fp16 = conv(dilations = var_10989, groups = var_6865, pad = k_223_pad_0, pad_type = k_223_pad_type_0, strides = var_10987, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_223_cast_fp16")]; + tensor var_10993 = const()[name = tensor("op_10993"), val = tensor([1, 1])]; + tensor var_10995 = const()[name = tensor("op_10995"), val = tensor([1, 1])]; + tensor v_223_pad_type_0 = const()[name = tensor("v_223_pad_type_0"), val = tensor("custom")]; + tensor v_223_pad_0 = const()[name = tensor("v_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1599827136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601793280))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_223_cast_fp16 = conv(dilations = var_10995, groups = var_6865, pad = v_223_pad_0, pad_type = v_223_pad_type_0, strides = var_10993, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_223_cast_fp16")]; + tensor var_10999 = const()[name = tensor("op_10999"), val = tensor([1, 20, 64, -1])]; + tensor var_11000_cast_fp16 = reshape(shape = var_10999, x = q_223_cast_fp16)[name = tensor("op_11000_cast_fp16")]; + tensor var_11001 = const()[name = tensor("op_11001"), val = tensor([1, 20, 64, -1])]; + tensor var_11002_cast_fp16 = reshape(shape = var_11001, x = k_223_cast_fp16)[name = tensor("op_11002_cast_fp16")]; + tensor var_11003 = const()[name = tensor("op_11003"), val = tensor([1, 20, 64, -1])]; + tensor var_11004_cast_fp16 = reshape(shape = var_11003, x = v_223_cast_fp16)[name = tensor("op_11004_cast_fp16")]; + tensor attn_weights_445_transpose_x_0 = const()[name = tensor("attn_weights_445_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_445_transpose_y_0 = const()[name = tensor("attn_weights_445_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_445_cast_fp16 = matmul(transpose_x = attn_weights_445_transpose_x_0, transpose_y = attn_weights_445_transpose_y_0, x = var_11000_cast_fp16, y = var_11002_cast_fp16)[name = tensor("attn_weights_445_cast_fp16")]; + tensor attn_weights_447_cast_fp16 = mul(x = attn_weights_445_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_447_cast_fp16")]; + tensor var_11008_cast_fp16 = softmax(axis = var_6849, x = attn_weights_447_cast_fp16)[name = tensor("op_11008_cast_fp16")]; + tensor attn_223_transpose_x_0 = const()[name = tensor("attn_223_transpose_x_0"), val = tensor(false)]; + tensor attn_223_transpose_y_0 = const()[name = tensor("attn_223_transpose_y_0"), val = tensor(true)]; + tensor attn_223_cast_fp16 = matmul(transpose_x = attn_223_transpose_x_0, transpose_y = attn_223_transpose_y_0, x = var_11004_cast_fp16, y = var_11008_cast_fp16)[name = tensor("attn_223_cast_fp16")]; + tensor var_11012 = const()[name = tensor("op_11012"), val = tensor([1, 1280, 1, -1])]; + tensor input_645_cast_fp16 = reshape(shape = var_11012, x = attn_223_cast_fp16)[name = tensor("input_645_cast_fp16")]; + tensor var_11017 = const()[name = tensor("op_11017"), val = tensor([1, 1])]; + tensor var_11019 = const()[name = tensor("op_11019"), val = tensor([1, 1])]; + tensor var_11021_pad_type_0 = const()[name = tensor("op_11021_pad_type_0"), val = tensor("custom")]; + tensor var_11021_pad_0 = const()[name = tensor("op_11021_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601793472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603022336))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603022528)))]; + tensor var_11021_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_11019, groups = var_6865, pad = var_11021_pad_0, pad_type = var_11021_pad_type_0, strides = var_11017, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_645_cast_fp16)[name = tensor("op_11021_cast_fp16")]; + tensor inputs_335_cast_fp16 = add(x = var_11021_cast_fp16, y = inputs_333_cast_fp16)[name = tensor("inputs_335_cast_fp16")]; + tensor input_647_axes_0 = const()[name = tensor("input_647_axes_0"), val = tensor([1])]; + tensor input_647_gamma_0_to_fp16 = const()[name = tensor("input_647_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603025152)))]; + tensor input_647_beta_0_to_fp16 = const()[name = tensor("input_647_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603027776)))]; + tensor var_11031_to_fp16 = const()[name = tensor("op_11031_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_647_cast_fp16 = layer_norm(axes = input_647_axes_0, beta = input_647_beta_0_to_fp16, epsilon = var_11031_to_fp16, gamma = input_647_gamma_0_to_fp16, x = inputs_335_cast_fp16)[name = tensor("input_647_cast_fp16")]; + tensor var_11047 = const()[name = tensor("op_11047"), val = tensor([1, 1])]; + tensor var_11049 = const()[name = tensor("op_11049"), val = tensor([1, 1])]; + tensor var_11051_pad_type_0 = const()[name = tensor("op_11051_pad_type_0"), val = tensor("custom")]; + tensor var_11051_pad_0 = const()[name = tensor("op_11051_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603030400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612860864))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612861056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612868800))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11051_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11049, groups = var_6865, pad = var_11051_pad_0, pad_type = var_11051_pad_type_0, strides = var_11047, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_647_cast_fp16)[name = tensor("op_11051_cast_fp16")]; + tensor var_11052_split_sizes_0 = const()[name = tensor("op_11052_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11052_axis_0 = const()[name = tensor("op_11052_axis_0"), val = tensor(1)]; + tensor var_11052_cast_fp16_0, tensor var_11052_cast_fp16_1 = split(axis = var_11052_axis_0, split_sizes = var_11052_split_sizes_0, x = var_11051_cast_fp16)[name = tensor("op_11052_cast_fp16")]; + tensor var_11054_mode_0 = const()[name = tensor("op_11054_mode_0"), val = tensor("EXACT")]; + tensor var_11054_cast_fp16 = gelu(mode = var_11054_mode_0, x = var_11052_cast_fp16_1)[name = tensor("op_11054_cast_fp16")]; + tensor input_649_cast_fp16 = mul(x = var_11052_cast_fp16_0, y = var_11054_cast_fp16)[name = tensor("input_649_cast_fp16")]; + tensor var_11058 = const()[name = tensor("op_11058"), val = tensor([1, 1])]; + tensor var_11060 = const()[name = tensor("op_11060"), val = tensor([1, 1])]; + tensor var_11062_pad_type_0 = const()[name = tensor("op_11062_pad_type_0"), val = tensor("custom")]; + tensor var_11062_pad_0 = const()[name = tensor("op_11062_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1612868992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617784256))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617784448)))]; + tensor var_11062_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_11060, groups = var_6865, pad = var_11062_pad_0, pad_type = var_11062_pad_type_0, strides = var_11058, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_649_cast_fp16)[name = tensor("op_11062_cast_fp16")]; + tensor inputs_337_cast_fp16 = add(x = var_11062_cast_fp16, y = inputs_335_cast_fp16)[name = tensor("inputs_337_cast_fp16")]; + tensor hidden_states_447_axes_0 = const()[name = tensor("hidden_states_447_axes_0"), val = tensor([1])]; + tensor hidden_states_447_gamma_0_to_fp16 = const()[name = tensor("hidden_states_447_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617787072)))]; + tensor hidden_states_447_beta_0_to_fp16 = const()[name = tensor("hidden_states_447_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617789696)))]; + tensor var_11078_to_fp16 = const()[name = tensor("op_11078_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_447_cast_fp16 = layer_norm(axes = hidden_states_447_axes_0, beta = hidden_states_447_beta_0_to_fp16, epsilon = var_11078_to_fp16, gamma = hidden_states_447_gamma_0_to_fp16, x = inputs_337_cast_fp16)[name = tensor("hidden_states_447_cast_fp16")]; + tensor var_11093 = const()[name = tensor("op_11093"), val = tensor([1, 1])]; + tensor var_11095 = const()[name = tensor("op_11095"), val = tensor([1, 1])]; + tensor q_225_pad_type_0 = const()[name = tensor("q_225_pad_type_0"), val = tensor("custom")]; + tensor q_225_pad_0 = const()[name = tensor("q_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617792320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1619021184))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_225_cast_fp16 = conv(dilations = var_11095, groups = var_6865, pad = q_225_pad_0, pad_type = q_225_pad_type_0, strides = var_11093, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_447_cast_fp16)[name = tensor("q_225_cast_fp16")]; + tensor var_11099 = const()[name = tensor("op_11099"), val = tensor([1, 1])]; + tensor var_11101 = const()[name = tensor("op_11101"), val = tensor([1, 1])]; + tensor k_225_pad_type_0 = const()[name = tensor("k_225_pad_type_0"), val = tensor("custom")]; + tensor k_225_pad_0 = const()[name = tensor("k_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1619021376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1620250240))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_225_cast_fp16 = conv(dilations = var_11101, groups = var_6865, pad = k_225_pad_0, pad_type = k_225_pad_type_0, strides = var_11099, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_447_cast_fp16)[name = tensor("k_225_cast_fp16")]; + tensor var_11105 = const()[name = tensor("op_11105"), val = tensor([1, 1])]; + tensor var_11107 = const()[name = tensor("op_11107"), val = tensor([1, 1])]; + tensor v_225_pad_type_0 = const()[name = tensor("v_225_pad_type_0"), val = tensor("custom")]; + tensor v_225_pad_0 = const()[name = tensor("v_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1620250432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1621479296))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_225_cast_fp16 = conv(dilations = var_11107, groups = var_6865, pad = v_225_pad_0, pad_type = v_225_pad_type_0, strides = var_11105, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_447_cast_fp16)[name = tensor("v_225_cast_fp16")]; + tensor var_11111 = const()[name = tensor("op_11111"), val = tensor([1, 20, 64, -1])]; + tensor var_11112_cast_fp16 = reshape(shape = var_11111, x = q_225_cast_fp16)[name = tensor("op_11112_cast_fp16")]; + tensor var_11113 = const()[name = tensor("op_11113"), val = tensor([1, 20, 64, -1])]; + tensor var_11114_cast_fp16 = reshape(shape = var_11113, x = k_225_cast_fp16)[name = tensor("op_11114_cast_fp16")]; + tensor var_11115 = const()[name = tensor("op_11115"), val = tensor([1, 20, 64, -1])]; + tensor var_11116_cast_fp16 = reshape(shape = var_11115, x = v_225_cast_fp16)[name = tensor("op_11116_cast_fp16")]; + tensor attn_weights_449_transpose_x_0 = const()[name = tensor("attn_weights_449_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_449_transpose_y_0 = const()[name = tensor("attn_weights_449_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_449_cast_fp16 = matmul(transpose_x = attn_weights_449_transpose_x_0, transpose_y = attn_weights_449_transpose_y_0, x = var_11112_cast_fp16, y = var_11114_cast_fp16)[name = tensor("attn_weights_449_cast_fp16")]; + tensor attn_weights_451_cast_fp16 = mul(x = attn_weights_449_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_451_cast_fp16")]; + tensor var_11120_cast_fp16 = softmax(axis = var_6849, x = attn_weights_451_cast_fp16)[name = tensor("op_11120_cast_fp16")]; + tensor attn_225_transpose_x_0 = const()[name = tensor("attn_225_transpose_x_0"), val = tensor(false)]; + tensor attn_225_transpose_y_0 = const()[name = tensor("attn_225_transpose_y_0"), val = tensor(true)]; + tensor attn_225_cast_fp16 = matmul(transpose_x = attn_225_transpose_x_0, transpose_y = attn_225_transpose_y_0, x = var_11116_cast_fp16, y = var_11120_cast_fp16)[name = tensor("attn_225_cast_fp16")]; + tensor var_11124 = const()[name = tensor("op_11124"), val = tensor([1, 1280, 1, -1])]; + tensor input_651_cast_fp16 = reshape(shape = var_11124, x = attn_225_cast_fp16)[name = tensor("input_651_cast_fp16")]; + tensor var_11129 = const()[name = tensor("op_11129"), val = tensor([1, 1])]; + tensor var_11131 = const()[name = tensor("op_11131"), val = tensor([1, 1])]; + tensor var_11133_pad_type_0 = const()[name = tensor("op_11133_pad_type_0"), val = tensor("custom")]; + tensor var_11133_pad_0 = const()[name = tensor("op_11133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1621479488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622708352))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622708544)))]; + tensor var_11133_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_11131, groups = var_6865, pad = var_11133_pad_0, pad_type = var_11133_pad_type_0, strides = var_11129, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16_palettized, x = input_651_cast_fp16)[name = tensor("op_11133_cast_fp16")]; + tensor inputs_339_cast_fp16 = add(x = var_11133_cast_fp16, y = inputs_337_cast_fp16)[name = tensor("inputs_339_cast_fp16")]; + tensor hidden_states_449_axes_0 = const()[name = tensor("hidden_states_449_axes_0"), val = tensor([1])]; + tensor hidden_states_449_gamma_0_to_fp16 = const()[name = tensor("hidden_states_449_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622711168)))]; + tensor hidden_states_449_beta_0_to_fp16 = const()[name = tensor("hidden_states_449_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622713792)))]; + tensor var_11143_to_fp16 = const()[name = tensor("op_11143_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_449_cast_fp16 = layer_norm(axes = hidden_states_449_axes_0, beta = hidden_states_449_beta_0_to_fp16, epsilon = var_11143_to_fp16, gamma = hidden_states_449_gamma_0_to_fp16, x = inputs_339_cast_fp16)[name = tensor("hidden_states_449_cast_fp16")]; + tensor var_11158 = const()[name = tensor("op_11158"), val = tensor([1, 1])]; + tensor var_11160 = const()[name = tensor("op_11160"), val = tensor([1, 1])]; + tensor q_227_pad_type_0 = const()[name = tensor("q_227_pad_type_0"), val = tensor("custom")]; + tensor q_227_pad_0 = const()[name = tensor("q_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1622716416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1623945280))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_227_cast_fp16 = conv(dilations = var_11160, groups = var_6865, pad = q_227_pad_0, pad_type = q_227_pad_type_0, strides = var_11158, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_449_cast_fp16)[name = tensor("q_227_cast_fp16")]; + tensor var_11164 = const()[name = tensor("op_11164"), val = tensor([1, 1])]; + tensor var_11166 = const()[name = tensor("op_11166"), val = tensor([1, 1])]; + tensor k_227_pad_type_0 = const()[name = tensor("k_227_pad_type_0"), val = tensor("custom")]; + tensor k_227_pad_0 = const()[name = tensor("k_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1623945472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1625911616))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_227_cast_fp16 = conv(dilations = var_11166, groups = var_6865, pad = k_227_pad_0, pad_type = k_227_pad_type_0, strides = var_11164, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_227_cast_fp16")]; + tensor var_11170 = const()[name = tensor("op_11170"), val = tensor([1, 1])]; + tensor var_11172 = const()[name = tensor("op_11172"), val = tensor([1, 1])]; + tensor v_227_pad_type_0 = const()[name = tensor("v_227_pad_type_0"), val = tensor("custom")]; + tensor v_227_pad_0 = const()[name = tensor("v_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1625911808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627877952))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_227_cast_fp16 = conv(dilations = var_11172, groups = var_6865, pad = v_227_pad_0, pad_type = v_227_pad_type_0, strides = var_11170, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_227_cast_fp16")]; + tensor var_11176 = const()[name = tensor("op_11176"), val = tensor([1, 20, 64, -1])]; + tensor var_11177_cast_fp16 = reshape(shape = var_11176, x = q_227_cast_fp16)[name = tensor("op_11177_cast_fp16")]; + tensor var_11178 = const()[name = tensor("op_11178"), val = tensor([1, 20, 64, -1])]; + tensor var_11179_cast_fp16 = reshape(shape = var_11178, x = k_227_cast_fp16)[name = tensor("op_11179_cast_fp16")]; + tensor var_11180 = const()[name = tensor("op_11180"), val = tensor([1, 20, 64, -1])]; + tensor var_11181_cast_fp16 = reshape(shape = var_11180, x = v_227_cast_fp16)[name = tensor("op_11181_cast_fp16")]; + tensor attn_weights_453_transpose_x_0 = const()[name = tensor("attn_weights_453_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_453_transpose_y_0 = const()[name = tensor("attn_weights_453_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_453_cast_fp16 = matmul(transpose_x = attn_weights_453_transpose_x_0, transpose_y = attn_weights_453_transpose_y_0, x = var_11177_cast_fp16, y = var_11179_cast_fp16)[name = tensor("attn_weights_453_cast_fp16")]; + tensor attn_weights_455_cast_fp16 = mul(x = attn_weights_453_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_455_cast_fp16")]; + tensor var_11185_cast_fp16 = softmax(axis = var_6849, x = attn_weights_455_cast_fp16)[name = tensor("op_11185_cast_fp16")]; + tensor attn_227_transpose_x_0 = const()[name = tensor("attn_227_transpose_x_0"), val = tensor(false)]; + tensor attn_227_transpose_y_0 = const()[name = tensor("attn_227_transpose_y_0"), val = tensor(true)]; + tensor attn_227_cast_fp16 = matmul(transpose_x = attn_227_transpose_x_0, transpose_y = attn_227_transpose_y_0, x = var_11181_cast_fp16, y = var_11185_cast_fp16)[name = tensor("attn_227_cast_fp16")]; + tensor var_11189 = const()[name = tensor("op_11189"), val = tensor([1, 1280, 1, -1])]; + tensor input_653_cast_fp16 = reshape(shape = var_11189, x = attn_227_cast_fp16)[name = tensor("input_653_cast_fp16")]; + tensor var_11194 = const()[name = tensor("op_11194"), val = tensor([1, 1])]; + tensor var_11196 = const()[name = tensor("op_11196"), val = tensor([1, 1])]; + tensor var_11198_pad_type_0 = const()[name = tensor("op_11198_pad_type_0"), val = tensor("custom")]; + tensor var_11198_pad_0 = const()[name = tensor("op_11198_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627878144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629107008))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629107200)))]; + tensor var_11198_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_11196, groups = var_6865, pad = var_11198_pad_0, pad_type = var_11198_pad_type_0, strides = var_11194, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16_palettized, x = input_653_cast_fp16)[name = tensor("op_11198_cast_fp16")]; + tensor inputs_341_cast_fp16 = add(x = var_11198_cast_fp16, y = inputs_339_cast_fp16)[name = tensor("inputs_341_cast_fp16")]; + tensor input_655_axes_0 = const()[name = tensor("input_655_axes_0"), val = tensor([1])]; + tensor input_655_gamma_0_to_fp16 = const()[name = tensor("input_655_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629109824)))]; + tensor input_655_beta_0_to_fp16 = const()[name = tensor("input_655_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629112448)))]; + tensor var_11208_to_fp16 = const()[name = tensor("op_11208_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_655_cast_fp16 = layer_norm(axes = input_655_axes_0, beta = input_655_beta_0_to_fp16, epsilon = var_11208_to_fp16, gamma = input_655_gamma_0_to_fp16, x = inputs_341_cast_fp16)[name = tensor("input_655_cast_fp16")]; + tensor var_11224 = const()[name = tensor("op_11224"), val = tensor([1, 1])]; + tensor var_11226 = const()[name = tensor("op_11226"), val = tensor([1, 1])]; + tensor var_11228_pad_type_0 = const()[name = tensor("op_11228_pad_type_0"), val = tensor("custom")]; + tensor var_11228_pad_0 = const()[name = tensor("op_11228_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629115072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638945536))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638945728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638953472))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11228_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11226, groups = var_6865, pad = var_11228_pad_0, pad_type = var_11228_pad_type_0, strides = var_11224, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16_palettized, x = input_655_cast_fp16)[name = tensor("op_11228_cast_fp16")]; + tensor var_11229_split_sizes_0 = const()[name = tensor("op_11229_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11229_axis_0 = const()[name = tensor("op_11229_axis_0"), val = tensor(1)]; + tensor var_11229_cast_fp16_0, tensor var_11229_cast_fp16_1 = split(axis = var_11229_axis_0, split_sizes = var_11229_split_sizes_0, x = var_11228_cast_fp16)[name = tensor("op_11229_cast_fp16")]; + tensor var_11231_mode_0 = const()[name = tensor("op_11231_mode_0"), val = tensor("EXACT")]; + tensor var_11231_cast_fp16 = gelu(mode = var_11231_mode_0, x = var_11229_cast_fp16_1)[name = tensor("op_11231_cast_fp16")]; + tensor input_657_cast_fp16 = mul(x = var_11229_cast_fp16_0, y = var_11231_cast_fp16)[name = tensor("input_657_cast_fp16")]; + tensor var_11235 = const()[name = tensor("op_11235"), val = tensor([1, 1])]; + tensor var_11237 = const()[name = tensor("op_11237"), val = tensor([1, 1])]; + tensor var_11239_pad_type_0 = const()[name = tensor("op_11239_pad_type_0"), val = tensor("custom")]; + tensor var_11239_pad_0 = const()[name = tensor("op_11239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1638953664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643868928))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643869120)))]; + tensor var_11239_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_11237, groups = var_6865, pad = var_11239_pad_0, pad_type = var_11239_pad_type_0, strides = var_11235, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16_palettized, x = input_657_cast_fp16)[name = tensor("op_11239_cast_fp16")]; + tensor inputs_343_cast_fp16 = add(x = var_11239_cast_fp16, y = inputs_341_cast_fp16)[name = tensor("inputs_343_cast_fp16")]; + tensor hidden_states_453_axes_0 = const()[name = tensor("hidden_states_453_axes_0"), val = tensor([1])]; + tensor hidden_states_453_gamma_0_to_fp16 = const()[name = tensor("hidden_states_453_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643871744)))]; + tensor hidden_states_453_beta_0_to_fp16 = const()[name = tensor("hidden_states_453_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643874368)))]; + tensor var_11255_to_fp16 = const()[name = tensor("op_11255_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_453_cast_fp16 = layer_norm(axes = hidden_states_453_axes_0, beta = hidden_states_453_beta_0_to_fp16, epsilon = var_11255_to_fp16, gamma = hidden_states_453_gamma_0_to_fp16, x = inputs_343_cast_fp16)[name = tensor("hidden_states_453_cast_fp16")]; + tensor var_11270 = const()[name = tensor("op_11270"), val = tensor([1, 1])]; + tensor var_11272 = const()[name = tensor("op_11272"), val = tensor([1, 1])]; + tensor q_229_pad_type_0 = const()[name = tensor("q_229_pad_type_0"), val = tensor("custom")]; + tensor q_229_pad_0 = const()[name = tensor("q_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643876992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645105856))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_229_cast_fp16 = conv(dilations = var_11272, groups = var_6865, pad = q_229_pad_0, pad_type = q_229_pad_type_0, strides = var_11270, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_453_cast_fp16)[name = tensor("q_229_cast_fp16")]; + tensor var_11276 = const()[name = tensor("op_11276"), val = tensor([1, 1])]; + tensor var_11278 = const()[name = tensor("op_11278"), val = tensor([1, 1])]; + tensor k_229_pad_type_0 = const()[name = tensor("k_229_pad_type_0"), val = tensor("custom")]; + tensor k_229_pad_0 = const()[name = tensor("k_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645106048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1646334912))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_229_cast_fp16 = conv(dilations = var_11278, groups = var_6865, pad = k_229_pad_0, pad_type = k_229_pad_type_0, strides = var_11276, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_453_cast_fp16)[name = tensor("k_229_cast_fp16")]; + tensor var_11282 = const()[name = tensor("op_11282"), val = tensor([1, 1])]; + tensor var_11284 = const()[name = tensor("op_11284"), val = tensor([1, 1])]; + tensor v_229_pad_type_0 = const()[name = tensor("v_229_pad_type_0"), val = tensor("custom")]; + tensor v_229_pad_0 = const()[name = tensor("v_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1646335104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647563968))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_229_cast_fp16 = conv(dilations = var_11284, groups = var_6865, pad = v_229_pad_0, pad_type = v_229_pad_type_0, strides = var_11282, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_453_cast_fp16)[name = tensor("v_229_cast_fp16")]; + tensor var_11288 = const()[name = tensor("op_11288"), val = tensor([1, 20, 64, -1])]; + tensor var_11289_cast_fp16 = reshape(shape = var_11288, x = q_229_cast_fp16)[name = tensor("op_11289_cast_fp16")]; + tensor var_11290 = const()[name = tensor("op_11290"), val = tensor([1, 20, 64, -1])]; + tensor var_11291_cast_fp16 = reshape(shape = var_11290, x = k_229_cast_fp16)[name = tensor("op_11291_cast_fp16")]; + tensor var_11292 = const()[name = tensor("op_11292"), val = tensor([1, 20, 64, -1])]; + tensor var_11293_cast_fp16 = reshape(shape = var_11292, x = v_229_cast_fp16)[name = tensor("op_11293_cast_fp16")]; + tensor attn_weights_457_transpose_x_0 = const()[name = tensor("attn_weights_457_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_457_transpose_y_0 = const()[name = tensor("attn_weights_457_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_457_cast_fp16 = matmul(transpose_x = attn_weights_457_transpose_x_0, transpose_y = attn_weights_457_transpose_y_0, x = var_11289_cast_fp16, y = var_11291_cast_fp16)[name = tensor("attn_weights_457_cast_fp16")]; + tensor attn_weights_459_cast_fp16 = mul(x = attn_weights_457_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_459_cast_fp16")]; + tensor var_11297_cast_fp16 = softmax(axis = var_6849, x = attn_weights_459_cast_fp16)[name = tensor("op_11297_cast_fp16")]; + tensor attn_229_transpose_x_0 = const()[name = tensor("attn_229_transpose_x_0"), val = tensor(false)]; + tensor attn_229_transpose_y_0 = const()[name = tensor("attn_229_transpose_y_0"), val = tensor(true)]; + tensor attn_229_cast_fp16 = matmul(transpose_x = attn_229_transpose_x_0, transpose_y = attn_229_transpose_y_0, x = var_11293_cast_fp16, y = var_11297_cast_fp16)[name = tensor("attn_229_cast_fp16")]; + tensor var_11301 = const()[name = tensor("op_11301"), val = tensor([1, 1280, 1, -1])]; + tensor input_659_cast_fp16 = reshape(shape = var_11301, x = attn_229_cast_fp16)[name = tensor("input_659_cast_fp16")]; + tensor var_11306 = const()[name = tensor("op_11306"), val = tensor([1, 1])]; + tensor var_11308 = const()[name = tensor("op_11308"), val = tensor([1, 1])]; + tensor var_11310_pad_type_0 = const()[name = tensor("op_11310_pad_type_0"), val = tensor("custom")]; + tensor var_11310_pad_0 = const()[name = tensor("op_11310_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1647564160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648793024))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648793216)))]; + tensor var_11310_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_11308, groups = var_6865, pad = var_11310_pad_0, pad_type = var_11310_pad_type_0, strides = var_11306, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16_palettized, x = input_659_cast_fp16)[name = tensor("op_11310_cast_fp16")]; + tensor inputs_345_cast_fp16 = add(x = var_11310_cast_fp16, y = inputs_343_cast_fp16)[name = tensor("inputs_345_cast_fp16")]; + tensor hidden_states_455_axes_0 = const()[name = tensor("hidden_states_455_axes_0"), val = tensor([1])]; + tensor hidden_states_455_gamma_0_to_fp16 = const()[name = tensor("hidden_states_455_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648795840)))]; + tensor hidden_states_455_beta_0_to_fp16 = const()[name = tensor("hidden_states_455_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648798464)))]; + tensor var_11320_to_fp16 = const()[name = tensor("op_11320_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_455_cast_fp16 = layer_norm(axes = hidden_states_455_axes_0, beta = hidden_states_455_beta_0_to_fp16, epsilon = var_11320_to_fp16, gamma = hidden_states_455_gamma_0_to_fp16, x = inputs_345_cast_fp16)[name = tensor("hidden_states_455_cast_fp16")]; + tensor var_11335 = const()[name = tensor("op_11335"), val = tensor([1, 1])]; + tensor var_11337 = const()[name = tensor("op_11337"), val = tensor([1, 1])]; + tensor q_231_pad_type_0 = const()[name = tensor("q_231_pad_type_0"), val = tensor("custom")]; + tensor q_231_pad_0 = const()[name = tensor("q_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648801088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1650029952))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_231_cast_fp16 = conv(dilations = var_11337, groups = var_6865, pad = q_231_pad_0, pad_type = q_231_pad_type_0, strides = var_11335, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_455_cast_fp16)[name = tensor("q_231_cast_fp16")]; + tensor var_11341 = const()[name = tensor("op_11341"), val = tensor([1, 1])]; + tensor var_11343 = const()[name = tensor("op_11343"), val = tensor([1, 1])]; + tensor k_231_pad_type_0 = const()[name = tensor("k_231_pad_type_0"), val = tensor("custom")]; + tensor k_231_pad_0 = const()[name = tensor("k_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1650030144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1651996288))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_231_cast_fp16 = conv(dilations = var_11343, groups = var_6865, pad = k_231_pad_0, pad_type = k_231_pad_type_0, strides = var_11341, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_231_cast_fp16")]; + tensor var_11347 = const()[name = tensor("op_11347"), val = tensor([1, 1])]; + tensor var_11349 = const()[name = tensor("op_11349"), val = tensor([1, 1])]; + tensor v_231_pad_type_0 = const()[name = tensor("v_231_pad_type_0"), val = tensor("custom")]; + tensor v_231_pad_0 = const()[name = tensor("v_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1651996480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1653962624))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_231_cast_fp16 = conv(dilations = var_11349, groups = var_6865, pad = v_231_pad_0, pad_type = v_231_pad_type_0, strides = var_11347, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_231_cast_fp16")]; + tensor var_11353 = const()[name = tensor("op_11353"), val = tensor([1, 20, 64, -1])]; + tensor var_11354_cast_fp16 = reshape(shape = var_11353, x = q_231_cast_fp16)[name = tensor("op_11354_cast_fp16")]; + tensor var_11355 = const()[name = tensor("op_11355"), val = tensor([1, 20, 64, -1])]; + tensor var_11356_cast_fp16 = reshape(shape = var_11355, x = k_231_cast_fp16)[name = tensor("op_11356_cast_fp16")]; + tensor var_11357 = const()[name = tensor("op_11357"), val = tensor([1, 20, 64, -1])]; + tensor var_11358_cast_fp16 = reshape(shape = var_11357, x = v_231_cast_fp16)[name = tensor("op_11358_cast_fp16")]; + tensor attn_weights_461_transpose_x_0 = const()[name = tensor("attn_weights_461_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_461_transpose_y_0 = const()[name = tensor("attn_weights_461_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_461_cast_fp16 = matmul(transpose_x = attn_weights_461_transpose_x_0, transpose_y = attn_weights_461_transpose_y_0, x = var_11354_cast_fp16, y = var_11356_cast_fp16)[name = tensor("attn_weights_461_cast_fp16")]; + tensor attn_weights_463_cast_fp16 = mul(x = attn_weights_461_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_463_cast_fp16")]; + tensor var_11362_cast_fp16 = softmax(axis = var_6849, x = attn_weights_463_cast_fp16)[name = tensor("op_11362_cast_fp16")]; + tensor attn_231_transpose_x_0 = const()[name = tensor("attn_231_transpose_x_0"), val = tensor(false)]; + tensor attn_231_transpose_y_0 = const()[name = tensor("attn_231_transpose_y_0"), val = tensor(true)]; + tensor attn_231_cast_fp16 = matmul(transpose_x = attn_231_transpose_x_0, transpose_y = attn_231_transpose_y_0, x = var_11358_cast_fp16, y = var_11362_cast_fp16)[name = tensor("attn_231_cast_fp16")]; + tensor var_11366 = const()[name = tensor("op_11366"), val = tensor([1, 1280, 1, -1])]; + tensor input_661_cast_fp16 = reshape(shape = var_11366, x = attn_231_cast_fp16)[name = tensor("input_661_cast_fp16")]; + tensor var_11371 = const()[name = tensor("op_11371"), val = tensor([1, 1])]; + tensor var_11373 = const()[name = tensor("op_11373"), val = tensor([1, 1])]; + tensor var_11375_pad_type_0 = const()[name = tensor("op_11375_pad_type_0"), val = tensor("custom")]; + tensor var_11375_pad_0 = const()[name = tensor("op_11375_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1653962816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655191680))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655191872)))]; + tensor var_11375_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_11373, groups = var_6865, pad = var_11375_pad_0, pad_type = var_11375_pad_type_0, strides = var_11371, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16_palettized, x = input_661_cast_fp16)[name = tensor("op_11375_cast_fp16")]; + tensor inputs_347_cast_fp16 = add(x = var_11375_cast_fp16, y = inputs_345_cast_fp16)[name = tensor("inputs_347_cast_fp16")]; + tensor input_663_axes_0 = const()[name = tensor("input_663_axes_0"), val = tensor([1])]; + tensor input_663_gamma_0_to_fp16 = const()[name = tensor("input_663_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655194496)))]; + tensor input_663_beta_0_to_fp16 = const()[name = tensor("input_663_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655197120)))]; + tensor var_11385_to_fp16 = const()[name = tensor("op_11385_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_663_cast_fp16 = layer_norm(axes = input_663_axes_0, beta = input_663_beta_0_to_fp16, epsilon = var_11385_to_fp16, gamma = input_663_gamma_0_to_fp16, x = inputs_347_cast_fp16)[name = tensor("input_663_cast_fp16")]; + tensor var_11401 = const()[name = tensor("op_11401"), val = tensor([1, 1])]; + tensor var_11403 = const()[name = tensor("op_11403"), val = tensor([1, 1])]; + tensor var_11405_pad_type_0 = const()[name = tensor("op_11405_pad_type_0"), val = tensor("custom")]; + tensor var_11405_pad_0 = const()[name = tensor("op_11405_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1655199744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665030208))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665030400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665038144))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11405_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11403, groups = var_6865, pad = var_11405_pad_0, pad_type = var_11405_pad_type_0, strides = var_11401, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16_palettized, x = input_663_cast_fp16)[name = tensor("op_11405_cast_fp16")]; + tensor var_11406_split_sizes_0 = const()[name = tensor("op_11406_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11406_axis_0 = const()[name = tensor("op_11406_axis_0"), val = tensor(1)]; + tensor var_11406_cast_fp16_0, tensor var_11406_cast_fp16_1 = split(axis = var_11406_axis_0, split_sizes = var_11406_split_sizes_0, x = var_11405_cast_fp16)[name = tensor("op_11406_cast_fp16")]; + tensor var_11408_mode_0 = const()[name = tensor("op_11408_mode_0"), val = tensor("EXACT")]; + tensor var_11408_cast_fp16 = gelu(mode = var_11408_mode_0, x = var_11406_cast_fp16_1)[name = tensor("op_11408_cast_fp16")]; + tensor input_665_cast_fp16 = mul(x = var_11406_cast_fp16_0, y = var_11408_cast_fp16)[name = tensor("input_665_cast_fp16")]; + tensor var_11412 = const()[name = tensor("op_11412"), val = tensor([1, 1])]; + tensor var_11414 = const()[name = tensor("op_11414"), val = tensor([1, 1])]; + tensor var_11416_pad_type_0 = const()[name = tensor("op_11416_pad_type_0"), val = tensor("custom")]; + tensor var_11416_pad_0 = const()[name = tensor("op_11416_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665038336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669953600))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669953792)))]; + tensor var_11416_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_11414, groups = var_6865, pad = var_11416_pad_0, pad_type = var_11416_pad_type_0, strides = var_11412, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16_palettized, x = input_665_cast_fp16)[name = tensor("op_11416_cast_fp16")]; + tensor inputs_349_cast_fp16 = add(x = var_11416_cast_fp16, y = inputs_347_cast_fp16)[name = tensor("inputs_349_cast_fp16")]; + tensor hidden_states_459_axes_0 = const()[name = tensor("hidden_states_459_axes_0"), val = tensor([1])]; + tensor hidden_states_459_gamma_0_to_fp16 = const()[name = tensor("hidden_states_459_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669956416)))]; + tensor hidden_states_459_beta_0_to_fp16 = const()[name = tensor("hidden_states_459_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669959040)))]; + tensor var_11432_to_fp16 = const()[name = tensor("op_11432_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_459_cast_fp16 = layer_norm(axes = hidden_states_459_axes_0, beta = hidden_states_459_beta_0_to_fp16, epsilon = var_11432_to_fp16, gamma = hidden_states_459_gamma_0_to_fp16, x = inputs_349_cast_fp16)[name = tensor("hidden_states_459_cast_fp16")]; + tensor var_11447 = const()[name = tensor("op_11447"), val = tensor([1, 1])]; + tensor var_11449 = const()[name = tensor("op_11449"), val = tensor([1, 1])]; + tensor q_233_pad_type_0 = const()[name = tensor("q_233_pad_type_0"), val = tensor("custom")]; + tensor q_233_pad_0 = const()[name = tensor("q_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669961664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1671190528))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_233_cast_fp16 = conv(dilations = var_11449, groups = var_6865, pad = q_233_pad_0, pad_type = q_233_pad_type_0, strides = var_11447, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_459_cast_fp16)[name = tensor("q_233_cast_fp16")]; + tensor var_11453 = const()[name = tensor("op_11453"), val = tensor([1, 1])]; + tensor var_11455 = const()[name = tensor("op_11455"), val = tensor([1, 1])]; + tensor k_233_pad_type_0 = const()[name = tensor("k_233_pad_type_0"), val = tensor("custom")]; + tensor k_233_pad_0 = const()[name = tensor("k_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1671190720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1672419584))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_233_cast_fp16 = conv(dilations = var_11455, groups = var_6865, pad = k_233_pad_0, pad_type = k_233_pad_type_0, strides = var_11453, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_459_cast_fp16)[name = tensor("k_233_cast_fp16")]; + tensor var_11459 = const()[name = tensor("op_11459"), val = tensor([1, 1])]; + tensor var_11461 = const()[name = tensor("op_11461"), val = tensor([1, 1])]; + tensor v_233_pad_type_0 = const()[name = tensor("v_233_pad_type_0"), val = tensor("custom")]; + tensor v_233_pad_0 = const()[name = tensor("v_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1672419776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1673648640))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_233_cast_fp16 = conv(dilations = var_11461, groups = var_6865, pad = v_233_pad_0, pad_type = v_233_pad_type_0, strides = var_11459, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_459_cast_fp16)[name = tensor("v_233_cast_fp16")]; + tensor var_11465 = const()[name = tensor("op_11465"), val = tensor([1, 20, 64, -1])]; + tensor var_11466_cast_fp16 = reshape(shape = var_11465, x = q_233_cast_fp16)[name = tensor("op_11466_cast_fp16")]; + tensor var_11467 = const()[name = tensor("op_11467"), val = tensor([1, 20, 64, -1])]; + tensor var_11468_cast_fp16 = reshape(shape = var_11467, x = k_233_cast_fp16)[name = tensor("op_11468_cast_fp16")]; + tensor var_11469 = const()[name = tensor("op_11469"), val = tensor([1, 20, 64, -1])]; + tensor var_11470_cast_fp16 = reshape(shape = var_11469, x = v_233_cast_fp16)[name = tensor("op_11470_cast_fp16")]; + tensor attn_weights_465_transpose_x_0 = const()[name = tensor("attn_weights_465_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_465_transpose_y_0 = const()[name = tensor("attn_weights_465_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_465_cast_fp16 = matmul(transpose_x = attn_weights_465_transpose_x_0, transpose_y = attn_weights_465_transpose_y_0, x = var_11466_cast_fp16, y = var_11468_cast_fp16)[name = tensor("attn_weights_465_cast_fp16")]; + tensor attn_weights_467_cast_fp16 = mul(x = attn_weights_465_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_467_cast_fp16")]; + tensor var_11474_cast_fp16 = softmax(axis = var_6849, x = attn_weights_467_cast_fp16)[name = tensor("op_11474_cast_fp16")]; + tensor attn_233_transpose_x_0 = const()[name = tensor("attn_233_transpose_x_0"), val = tensor(false)]; + tensor attn_233_transpose_y_0 = const()[name = tensor("attn_233_transpose_y_0"), val = tensor(true)]; + tensor attn_233_cast_fp16 = matmul(transpose_x = attn_233_transpose_x_0, transpose_y = attn_233_transpose_y_0, x = var_11470_cast_fp16, y = var_11474_cast_fp16)[name = tensor("attn_233_cast_fp16")]; + tensor var_11478 = const()[name = tensor("op_11478"), val = tensor([1, 1280, 1, -1])]; + tensor input_667_cast_fp16 = reshape(shape = var_11478, x = attn_233_cast_fp16)[name = tensor("input_667_cast_fp16")]; + tensor var_11483 = const()[name = tensor("op_11483"), val = tensor([1, 1])]; + tensor var_11485 = const()[name = tensor("op_11485"), val = tensor([1, 1])]; + tensor var_11487_pad_type_0 = const()[name = tensor("op_11487_pad_type_0"), val = tensor("custom")]; + tensor var_11487_pad_0 = const()[name = tensor("op_11487_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1673648832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674877696))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674877888)))]; + tensor var_11487_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_11485, groups = var_6865, pad = var_11487_pad_0, pad_type = var_11487_pad_type_0, strides = var_11483, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = tensor("op_11487_cast_fp16")]; + tensor inputs_351_cast_fp16 = add(x = var_11487_cast_fp16, y = inputs_349_cast_fp16)[name = tensor("inputs_351_cast_fp16")]; + tensor hidden_states_461_axes_0 = const()[name = tensor("hidden_states_461_axes_0"), val = tensor([1])]; + tensor hidden_states_461_gamma_0_to_fp16 = const()[name = tensor("hidden_states_461_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674880512)))]; + tensor hidden_states_461_beta_0_to_fp16 = const()[name = tensor("hidden_states_461_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674883136)))]; + tensor var_11497_to_fp16 = const()[name = tensor("op_11497_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_461_cast_fp16 = layer_norm(axes = hidden_states_461_axes_0, beta = hidden_states_461_beta_0_to_fp16, epsilon = var_11497_to_fp16, gamma = hidden_states_461_gamma_0_to_fp16, x = inputs_351_cast_fp16)[name = tensor("hidden_states_461_cast_fp16")]; + tensor var_11512 = const()[name = tensor("op_11512"), val = tensor([1, 1])]; + tensor var_11514 = const()[name = tensor("op_11514"), val = tensor([1, 1])]; + tensor q_235_pad_type_0 = const()[name = tensor("q_235_pad_type_0"), val = tensor("custom")]; + tensor q_235_pad_0 = const()[name = tensor("q_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674885760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1676114624))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_235_cast_fp16 = conv(dilations = var_11514, groups = var_6865, pad = q_235_pad_0, pad_type = q_235_pad_type_0, strides = var_11512, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_461_cast_fp16)[name = tensor("q_235_cast_fp16")]; + tensor var_11518 = const()[name = tensor("op_11518"), val = tensor([1, 1])]; + tensor var_11520 = const()[name = tensor("op_11520"), val = tensor([1, 1])]; + tensor k_235_pad_type_0 = const()[name = tensor("k_235_pad_type_0"), val = tensor("custom")]; + tensor k_235_pad_0 = const()[name = tensor("k_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1676114816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1678080960))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_235_cast_fp16 = conv(dilations = var_11520, groups = var_6865, pad = k_235_pad_0, pad_type = k_235_pad_type_0, strides = var_11518, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_235_cast_fp16")]; + tensor var_11524 = const()[name = tensor("op_11524"), val = tensor([1, 1])]; + tensor var_11526 = const()[name = tensor("op_11526"), val = tensor([1, 1])]; + tensor v_235_pad_type_0 = const()[name = tensor("v_235_pad_type_0"), val = tensor("custom")]; + tensor v_235_pad_0 = const()[name = tensor("v_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1678081152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1680047296))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_235_cast_fp16 = conv(dilations = var_11526, groups = var_6865, pad = v_235_pad_0, pad_type = v_235_pad_type_0, strides = var_11524, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_235_cast_fp16")]; + tensor var_11530 = const()[name = tensor("op_11530"), val = tensor([1, 20, 64, -1])]; + tensor var_11531_cast_fp16 = reshape(shape = var_11530, x = q_235_cast_fp16)[name = tensor("op_11531_cast_fp16")]; + tensor var_11532 = const()[name = tensor("op_11532"), val = tensor([1, 20, 64, -1])]; + tensor var_11533_cast_fp16 = reshape(shape = var_11532, x = k_235_cast_fp16)[name = tensor("op_11533_cast_fp16")]; + tensor var_11534 = const()[name = tensor("op_11534"), val = tensor([1, 20, 64, -1])]; + tensor var_11535_cast_fp16 = reshape(shape = var_11534, x = v_235_cast_fp16)[name = tensor("op_11535_cast_fp16")]; + tensor attn_weights_469_transpose_x_0 = const()[name = tensor("attn_weights_469_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_469_transpose_y_0 = const()[name = tensor("attn_weights_469_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_469_cast_fp16 = matmul(transpose_x = attn_weights_469_transpose_x_0, transpose_y = attn_weights_469_transpose_y_0, x = var_11531_cast_fp16, y = var_11533_cast_fp16)[name = tensor("attn_weights_469_cast_fp16")]; + tensor attn_weights_471_cast_fp16 = mul(x = attn_weights_469_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_471_cast_fp16")]; + tensor var_11539_cast_fp16 = softmax(axis = var_6849, x = attn_weights_471_cast_fp16)[name = tensor("op_11539_cast_fp16")]; + tensor attn_235_transpose_x_0 = const()[name = tensor("attn_235_transpose_x_0"), val = tensor(false)]; + tensor attn_235_transpose_y_0 = const()[name = tensor("attn_235_transpose_y_0"), val = tensor(true)]; + tensor attn_235_cast_fp16 = matmul(transpose_x = attn_235_transpose_x_0, transpose_y = attn_235_transpose_y_0, x = var_11535_cast_fp16, y = var_11539_cast_fp16)[name = tensor("attn_235_cast_fp16")]; + tensor var_11543 = const()[name = tensor("op_11543"), val = tensor([1, 1280, 1, -1])]; + tensor input_669_cast_fp16 = reshape(shape = var_11543, x = attn_235_cast_fp16)[name = tensor("input_669_cast_fp16")]; + tensor var_11548 = const()[name = tensor("op_11548"), val = tensor([1, 1])]; + tensor var_11550 = const()[name = tensor("op_11550"), val = tensor([1, 1])]; + tensor var_11552_pad_type_0 = const()[name = tensor("op_11552_pad_type_0"), val = tensor("custom")]; + tensor var_11552_pad_0 = const()[name = tensor("op_11552_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1680047488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681276352))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681276544)))]; + tensor var_11552_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_11550, groups = var_6865, pad = var_11552_pad_0, pad_type = var_11552_pad_type_0, strides = var_11548, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16_palettized, x = input_669_cast_fp16)[name = tensor("op_11552_cast_fp16")]; + tensor inputs_353_cast_fp16 = add(x = var_11552_cast_fp16, y = inputs_351_cast_fp16)[name = tensor("inputs_353_cast_fp16")]; + tensor input_671_axes_0 = const()[name = tensor("input_671_axes_0"), val = tensor([1])]; + tensor input_671_gamma_0_to_fp16 = const()[name = tensor("input_671_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681279168)))]; + tensor input_671_beta_0_to_fp16 = const()[name = tensor("input_671_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681281792)))]; + tensor var_11562_to_fp16 = const()[name = tensor("op_11562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_671_cast_fp16 = layer_norm(axes = input_671_axes_0, beta = input_671_beta_0_to_fp16, epsilon = var_11562_to_fp16, gamma = input_671_gamma_0_to_fp16, x = inputs_353_cast_fp16)[name = tensor("input_671_cast_fp16")]; + tensor var_11578 = const()[name = tensor("op_11578"), val = tensor([1, 1])]; + tensor var_11580 = const()[name = tensor("op_11580"), val = tensor([1, 1])]; + tensor var_11582_pad_type_0 = const()[name = tensor("op_11582_pad_type_0"), val = tensor("custom")]; + tensor var_11582_pad_0 = const()[name = tensor("op_11582_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681284416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691114880))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691115072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691122816))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11582_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11580, groups = var_6865, pad = var_11582_pad_0, pad_type = var_11582_pad_type_0, strides = var_11578, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16_palettized, x = input_671_cast_fp16)[name = tensor("op_11582_cast_fp16")]; + tensor var_11583_split_sizes_0 = const()[name = tensor("op_11583_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11583_axis_0 = const()[name = tensor("op_11583_axis_0"), val = tensor(1)]; + tensor var_11583_cast_fp16_0, tensor var_11583_cast_fp16_1 = split(axis = var_11583_axis_0, split_sizes = var_11583_split_sizes_0, x = var_11582_cast_fp16)[name = tensor("op_11583_cast_fp16")]; + tensor var_11585_mode_0 = const()[name = tensor("op_11585_mode_0"), val = tensor("EXACT")]; + tensor var_11585_cast_fp16 = gelu(mode = var_11585_mode_0, x = var_11583_cast_fp16_1)[name = tensor("op_11585_cast_fp16")]; + tensor input_673_cast_fp16 = mul(x = var_11583_cast_fp16_0, y = var_11585_cast_fp16)[name = tensor("input_673_cast_fp16")]; + tensor var_11589 = const()[name = tensor("op_11589"), val = tensor([1, 1])]; + tensor var_11591 = const()[name = tensor("op_11591"), val = tensor([1, 1])]; + tensor var_11593_pad_type_0 = const()[name = tensor("op_11593_pad_type_0"), val = tensor("custom")]; + tensor var_11593_pad_0 = const()[name = tensor("op_11593_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1691123008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696038272))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696038464)))]; + tensor var_11593_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_11591, groups = var_6865, pad = var_11593_pad_0, pad_type = var_11593_pad_type_0, strides = var_11589, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16_palettized, x = input_673_cast_fp16)[name = tensor("op_11593_cast_fp16")]; + tensor inputs_355_cast_fp16 = add(x = var_11593_cast_fp16, y = inputs_353_cast_fp16)[name = tensor("inputs_355_cast_fp16")]; + tensor hidden_states_465_axes_0 = const()[name = tensor("hidden_states_465_axes_0"), val = tensor([1])]; + tensor hidden_states_465_gamma_0_to_fp16 = const()[name = tensor("hidden_states_465_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696041088)))]; + tensor hidden_states_465_beta_0_to_fp16 = const()[name = tensor("hidden_states_465_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696043712)))]; + tensor var_11609_to_fp16 = const()[name = tensor("op_11609_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_465_cast_fp16 = layer_norm(axes = hidden_states_465_axes_0, beta = hidden_states_465_beta_0_to_fp16, epsilon = var_11609_to_fp16, gamma = hidden_states_465_gamma_0_to_fp16, x = inputs_355_cast_fp16)[name = tensor("hidden_states_465_cast_fp16")]; + tensor var_11624 = const()[name = tensor("op_11624"), val = tensor([1, 1])]; + tensor var_11626 = const()[name = tensor("op_11626"), val = tensor([1, 1])]; + tensor q_237_pad_type_0 = const()[name = tensor("q_237_pad_type_0"), val = tensor("custom")]; + tensor q_237_pad_0 = const()[name = tensor("q_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1696046336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1697275200))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_237_cast_fp16 = conv(dilations = var_11626, groups = var_6865, pad = q_237_pad_0, pad_type = q_237_pad_type_0, strides = var_11624, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_465_cast_fp16)[name = tensor("q_237_cast_fp16")]; + tensor var_11630 = const()[name = tensor("op_11630"), val = tensor([1, 1])]; + tensor var_11632 = const()[name = tensor("op_11632"), val = tensor([1, 1])]; + tensor k_237_pad_type_0 = const()[name = tensor("k_237_pad_type_0"), val = tensor("custom")]; + tensor k_237_pad_0 = const()[name = tensor("k_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1697275392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698504256))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_237_cast_fp16 = conv(dilations = var_11632, groups = var_6865, pad = k_237_pad_0, pad_type = k_237_pad_type_0, strides = var_11630, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_465_cast_fp16)[name = tensor("k_237_cast_fp16")]; + tensor var_11636 = const()[name = tensor("op_11636"), val = tensor([1, 1])]; + tensor var_11638 = const()[name = tensor("op_11638"), val = tensor([1, 1])]; + tensor v_237_pad_type_0 = const()[name = tensor("v_237_pad_type_0"), val = tensor("custom")]; + tensor v_237_pad_0 = const()[name = tensor("v_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698504448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699733312))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_237_cast_fp16 = conv(dilations = var_11638, groups = var_6865, pad = v_237_pad_0, pad_type = v_237_pad_type_0, strides = var_11636, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_465_cast_fp16)[name = tensor("v_237_cast_fp16")]; + tensor var_11642 = const()[name = tensor("op_11642"), val = tensor([1, 20, 64, -1])]; + tensor var_11643_cast_fp16 = reshape(shape = var_11642, x = q_237_cast_fp16)[name = tensor("op_11643_cast_fp16")]; + tensor var_11644 = const()[name = tensor("op_11644"), val = tensor([1, 20, 64, -1])]; + tensor var_11645_cast_fp16 = reshape(shape = var_11644, x = k_237_cast_fp16)[name = tensor("op_11645_cast_fp16")]; + tensor var_11646 = const()[name = tensor("op_11646"), val = tensor([1, 20, 64, -1])]; + tensor var_11647_cast_fp16 = reshape(shape = var_11646, x = v_237_cast_fp16)[name = tensor("op_11647_cast_fp16")]; + tensor attn_weights_473_transpose_x_0 = const()[name = tensor("attn_weights_473_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_473_transpose_y_0 = const()[name = tensor("attn_weights_473_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_473_cast_fp16 = matmul(transpose_x = attn_weights_473_transpose_x_0, transpose_y = attn_weights_473_transpose_y_0, x = var_11643_cast_fp16, y = var_11645_cast_fp16)[name = tensor("attn_weights_473_cast_fp16")]; + tensor attn_weights_475_cast_fp16 = mul(x = attn_weights_473_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_475_cast_fp16")]; + tensor var_11651_cast_fp16 = softmax(axis = var_6849, x = attn_weights_475_cast_fp16)[name = tensor("op_11651_cast_fp16")]; + tensor attn_237_transpose_x_0 = const()[name = tensor("attn_237_transpose_x_0"), val = tensor(false)]; + tensor attn_237_transpose_y_0 = const()[name = tensor("attn_237_transpose_y_0"), val = tensor(true)]; + tensor attn_237_cast_fp16 = matmul(transpose_x = attn_237_transpose_x_0, transpose_y = attn_237_transpose_y_0, x = var_11647_cast_fp16, y = var_11651_cast_fp16)[name = tensor("attn_237_cast_fp16")]; + tensor var_11655 = const()[name = tensor("op_11655"), val = tensor([1, 1280, 1, -1])]; + tensor input_675_cast_fp16 = reshape(shape = var_11655, x = attn_237_cast_fp16)[name = tensor("input_675_cast_fp16")]; + tensor var_11660 = const()[name = tensor("op_11660"), val = tensor([1, 1])]; + tensor var_11662 = const()[name = tensor("op_11662"), val = tensor([1, 1])]; + tensor var_11664_pad_type_0 = const()[name = tensor("op_11664_pad_type_0"), val = tensor("custom")]; + tensor var_11664_pad_0 = const()[name = tensor("op_11664_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699733504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700962368))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700962560)))]; + tensor var_11664_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_11662, groups = var_6865, pad = var_11664_pad_0, pad_type = var_11664_pad_type_0, strides = var_11660, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16_palettized, x = input_675_cast_fp16)[name = tensor("op_11664_cast_fp16")]; + tensor inputs_357_cast_fp16 = add(x = var_11664_cast_fp16, y = inputs_355_cast_fp16)[name = tensor("inputs_357_cast_fp16")]; + tensor hidden_states_467_axes_0 = const()[name = tensor("hidden_states_467_axes_0"), val = tensor([1])]; + tensor hidden_states_467_gamma_0_to_fp16 = const()[name = tensor("hidden_states_467_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700965184)))]; + tensor hidden_states_467_beta_0_to_fp16 = const()[name = tensor("hidden_states_467_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700967808)))]; + tensor var_11674_to_fp16 = const()[name = tensor("op_11674_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_467_cast_fp16 = layer_norm(axes = hidden_states_467_axes_0, beta = hidden_states_467_beta_0_to_fp16, epsilon = var_11674_to_fp16, gamma = hidden_states_467_gamma_0_to_fp16, x = inputs_357_cast_fp16)[name = tensor("hidden_states_467_cast_fp16")]; + tensor var_11689 = const()[name = tensor("op_11689"), val = tensor([1, 1])]; + tensor var_11691 = const()[name = tensor("op_11691"), val = tensor([1, 1])]; + tensor q_239_pad_type_0 = const()[name = tensor("q_239_pad_type_0"), val = tensor("custom")]; + tensor q_239_pad_0 = const()[name = tensor("q_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1700970432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702199296))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_239_cast_fp16 = conv(dilations = var_11691, groups = var_6865, pad = q_239_pad_0, pad_type = q_239_pad_type_0, strides = var_11689, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_467_cast_fp16)[name = tensor("q_239_cast_fp16")]; + tensor var_11695 = const()[name = tensor("op_11695"), val = tensor([1, 1])]; + tensor var_11697 = const()[name = tensor("op_11697"), val = tensor([1, 1])]; + tensor k_239_pad_type_0 = const()[name = tensor("k_239_pad_type_0"), val = tensor("custom")]; + tensor k_239_pad_0 = const()[name = tensor("k_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702199488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704165632))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_239_cast_fp16 = conv(dilations = var_11697, groups = var_6865, pad = k_239_pad_0, pad_type = k_239_pad_type_0, strides = var_11695, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_239_cast_fp16")]; + tensor var_11701 = const()[name = tensor("op_11701"), val = tensor([1, 1])]; + tensor var_11703 = const()[name = tensor("op_11703"), val = tensor([1, 1])]; + tensor v_239_pad_type_0 = const()[name = tensor("v_239_pad_type_0"), val = tensor("custom")]; + tensor v_239_pad_0 = const()[name = tensor("v_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704165824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1706131968))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_239_cast_fp16 = conv(dilations = var_11703, groups = var_6865, pad = v_239_pad_0, pad_type = v_239_pad_type_0, strides = var_11701, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_239_cast_fp16")]; + tensor var_11707 = const()[name = tensor("op_11707"), val = tensor([1, 20, 64, -1])]; + tensor var_11708_cast_fp16 = reshape(shape = var_11707, x = q_239_cast_fp16)[name = tensor("op_11708_cast_fp16")]; + tensor var_11709 = const()[name = tensor("op_11709"), val = tensor([1, 20, 64, -1])]; + tensor var_11710_cast_fp16 = reshape(shape = var_11709, x = k_239_cast_fp16)[name = tensor("op_11710_cast_fp16")]; + tensor var_11711 = const()[name = tensor("op_11711"), val = tensor([1, 20, 64, -1])]; + tensor var_11712_cast_fp16 = reshape(shape = var_11711, x = v_239_cast_fp16)[name = tensor("op_11712_cast_fp16")]; + tensor attn_weights_477_transpose_x_0 = const()[name = tensor("attn_weights_477_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_477_transpose_y_0 = const()[name = tensor("attn_weights_477_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_477_cast_fp16 = matmul(transpose_x = attn_weights_477_transpose_x_0, transpose_y = attn_weights_477_transpose_y_0, x = var_11708_cast_fp16, y = var_11710_cast_fp16)[name = tensor("attn_weights_477_cast_fp16")]; + tensor attn_weights_479_cast_fp16 = mul(x = attn_weights_477_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_479_cast_fp16")]; + tensor var_11716_cast_fp16 = softmax(axis = var_6849, x = attn_weights_479_cast_fp16)[name = tensor("op_11716_cast_fp16")]; + tensor attn_239_transpose_x_0 = const()[name = tensor("attn_239_transpose_x_0"), val = tensor(false)]; + tensor attn_239_transpose_y_0 = const()[name = tensor("attn_239_transpose_y_0"), val = tensor(true)]; + tensor attn_239_cast_fp16 = matmul(transpose_x = attn_239_transpose_x_0, transpose_y = attn_239_transpose_y_0, x = var_11712_cast_fp16, y = var_11716_cast_fp16)[name = tensor("attn_239_cast_fp16")]; + tensor var_11720 = const()[name = tensor("op_11720"), val = tensor([1, 1280, 1, -1])]; + tensor input_677_cast_fp16 = reshape(shape = var_11720, x = attn_239_cast_fp16)[name = tensor("input_677_cast_fp16")]; + tensor var_11725 = const()[name = tensor("op_11725"), val = tensor([1, 1])]; + tensor var_11727 = const()[name = tensor("op_11727"), val = tensor([1, 1])]; + tensor var_11729_pad_type_0 = const()[name = tensor("op_11729_pad_type_0"), val = tensor("custom")]; + tensor var_11729_pad_0 = const()[name = tensor("op_11729_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1706132160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707361024))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707361216)))]; + tensor var_11729_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_11727, groups = var_6865, pad = var_11729_pad_0, pad_type = var_11729_pad_type_0, strides = var_11725, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16_palettized, x = input_677_cast_fp16)[name = tensor("op_11729_cast_fp16")]; + tensor inputs_359_cast_fp16 = add(x = var_11729_cast_fp16, y = inputs_357_cast_fp16)[name = tensor("inputs_359_cast_fp16")]; + tensor input_679_axes_0 = const()[name = tensor("input_679_axes_0"), val = tensor([1])]; + tensor input_679_gamma_0_to_fp16 = const()[name = tensor("input_679_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707363840)))]; + tensor input_679_beta_0_to_fp16 = const()[name = tensor("input_679_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707366464)))]; + tensor var_11739_to_fp16 = const()[name = tensor("op_11739_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_679_cast_fp16 = layer_norm(axes = input_679_axes_0, beta = input_679_beta_0_to_fp16, epsilon = var_11739_to_fp16, gamma = input_679_gamma_0_to_fp16, x = inputs_359_cast_fp16)[name = tensor("input_679_cast_fp16")]; + tensor var_11755 = const()[name = tensor("op_11755"), val = tensor([1, 1])]; + tensor var_11757 = const()[name = tensor("op_11757"), val = tensor([1, 1])]; + tensor var_11759_pad_type_0 = const()[name = tensor("op_11759_pad_type_0"), val = tensor("custom")]; + tensor var_11759_pad_0 = const()[name = tensor("op_11759_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707369088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717199552))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717199744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717207488))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11759_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11757, groups = var_6865, pad = var_11759_pad_0, pad_type = var_11759_pad_type_0, strides = var_11755, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16_palettized, x = input_679_cast_fp16)[name = tensor("op_11759_cast_fp16")]; + tensor var_11760_split_sizes_0 = const()[name = tensor("op_11760_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11760_axis_0 = const()[name = tensor("op_11760_axis_0"), val = tensor(1)]; + tensor var_11760_cast_fp16_0, tensor var_11760_cast_fp16_1 = split(axis = var_11760_axis_0, split_sizes = var_11760_split_sizes_0, x = var_11759_cast_fp16)[name = tensor("op_11760_cast_fp16")]; + tensor var_11762_mode_0 = const()[name = tensor("op_11762_mode_0"), val = tensor("EXACT")]; + tensor var_11762_cast_fp16 = gelu(mode = var_11762_mode_0, x = var_11760_cast_fp16_1)[name = tensor("op_11762_cast_fp16")]; + tensor input_681_cast_fp16 = mul(x = var_11760_cast_fp16_0, y = var_11762_cast_fp16)[name = tensor("input_681_cast_fp16")]; + tensor var_11766 = const()[name = tensor("op_11766"), val = tensor([1, 1])]; + tensor var_11768 = const()[name = tensor("op_11768"), val = tensor([1, 1])]; + tensor var_11770_pad_type_0 = const()[name = tensor("op_11770_pad_type_0"), val = tensor("custom")]; + tensor var_11770_pad_0 = const()[name = tensor("op_11770_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717207680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722122944))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722123136)))]; + tensor var_11770_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_11768, groups = var_6865, pad = var_11770_pad_0, pad_type = var_11770_pad_type_0, strides = var_11766, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16_palettized, x = input_681_cast_fp16)[name = tensor("op_11770_cast_fp16")]; + tensor inputs_361_cast_fp16 = add(x = var_11770_cast_fp16, y = inputs_359_cast_fp16)[name = tensor("inputs_361_cast_fp16")]; + tensor hidden_states_471_axes_0 = const()[name = tensor("hidden_states_471_axes_0"), val = tensor([1])]; + tensor hidden_states_471_gamma_0_to_fp16 = const()[name = tensor("hidden_states_471_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722125760)))]; + tensor hidden_states_471_beta_0_to_fp16 = const()[name = tensor("hidden_states_471_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722128384)))]; + tensor var_11786_to_fp16 = const()[name = tensor("op_11786_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_471_cast_fp16 = layer_norm(axes = hidden_states_471_axes_0, beta = hidden_states_471_beta_0_to_fp16, epsilon = var_11786_to_fp16, gamma = hidden_states_471_gamma_0_to_fp16, x = inputs_361_cast_fp16)[name = tensor("hidden_states_471_cast_fp16")]; + tensor var_11801 = const()[name = tensor("op_11801"), val = tensor([1, 1])]; + tensor var_11803 = const()[name = tensor("op_11803"), val = tensor([1, 1])]; + tensor q_241_pad_type_0 = const()[name = tensor("q_241_pad_type_0"), val = tensor("custom")]; + tensor q_241_pad_0 = const()[name = tensor("q_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722131008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1723359872))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_241_cast_fp16 = conv(dilations = var_11803, groups = var_6865, pad = q_241_pad_0, pad_type = q_241_pad_type_0, strides = var_11801, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_471_cast_fp16)[name = tensor("q_241_cast_fp16")]; + tensor var_11807 = const()[name = tensor("op_11807"), val = tensor([1, 1])]; + tensor var_11809 = const()[name = tensor("op_11809"), val = tensor([1, 1])]; + tensor k_241_pad_type_0 = const()[name = tensor("k_241_pad_type_0"), val = tensor("custom")]; + tensor k_241_pad_0 = const()[name = tensor("k_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1723360064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724588928))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_241_cast_fp16 = conv(dilations = var_11809, groups = var_6865, pad = k_241_pad_0, pad_type = k_241_pad_type_0, strides = var_11807, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_471_cast_fp16)[name = tensor("k_241_cast_fp16")]; + tensor var_11813 = const()[name = tensor("op_11813"), val = tensor([1, 1])]; + tensor var_11815 = const()[name = tensor("op_11815"), val = tensor([1, 1])]; + tensor v_241_pad_type_0 = const()[name = tensor("v_241_pad_type_0"), val = tensor("custom")]; + tensor v_241_pad_0 = const()[name = tensor("v_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724589120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1725817984))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_241_cast_fp16 = conv(dilations = var_11815, groups = var_6865, pad = v_241_pad_0, pad_type = v_241_pad_type_0, strides = var_11813, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_471_cast_fp16)[name = tensor("v_241_cast_fp16")]; + tensor var_11819 = const()[name = tensor("op_11819"), val = tensor([1, 20, 64, -1])]; + tensor var_11820_cast_fp16 = reshape(shape = var_11819, x = q_241_cast_fp16)[name = tensor("op_11820_cast_fp16")]; + tensor var_11821 = const()[name = tensor("op_11821"), val = tensor([1, 20, 64, -1])]; + tensor var_11822_cast_fp16 = reshape(shape = var_11821, x = k_241_cast_fp16)[name = tensor("op_11822_cast_fp16")]; + tensor var_11823 = const()[name = tensor("op_11823"), val = tensor([1, 20, 64, -1])]; + tensor var_11824_cast_fp16 = reshape(shape = var_11823, x = v_241_cast_fp16)[name = tensor("op_11824_cast_fp16")]; + tensor attn_weights_481_transpose_x_0 = const()[name = tensor("attn_weights_481_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_481_transpose_y_0 = const()[name = tensor("attn_weights_481_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_481_cast_fp16 = matmul(transpose_x = attn_weights_481_transpose_x_0, transpose_y = attn_weights_481_transpose_y_0, x = var_11820_cast_fp16, y = var_11822_cast_fp16)[name = tensor("attn_weights_481_cast_fp16")]; + tensor attn_weights_483_cast_fp16 = mul(x = attn_weights_481_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_483_cast_fp16")]; + tensor var_11828_cast_fp16 = softmax(axis = var_6849, x = attn_weights_483_cast_fp16)[name = tensor("op_11828_cast_fp16")]; + tensor attn_241_transpose_x_0 = const()[name = tensor("attn_241_transpose_x_0"), val = tensor(false)]; + tensor attn_241_transpose_y_0 = const()[name = tensor("attn_241_transpose_y_0"), val = tensor(true)]; + tensor attn_241_cast_fp16 = matmul(transpose_x = attn_241_transpose_x_0, transpose_y = attn_241_transpose_y_0, x = var_11824_cast_fp16, y = var_11828_cast_fp16)[name = tensor("attn_241_cast_fp16")]; + tensor var_11832 = const()[name = tensor("op_11832"), val = tensor([1, 1280, 1, -1])]; + tensor input_683_cast_fp16 = reshape(shape = var_11832, x = attn_241_cast_fp16)[name = tensor("input_683_cast_fp16")]; + tensor var_11837 = const()[name = tensor("op_11837"), val = tensor([1, 1])]; + tensor var_11839 = const()[name = tensor("op_11839"), val = tensor([1, 1])]; + tensor var_11841_pad_type_0 = const()[name = tensor("op_11841_pad_type_0"), val = tensor("custom")]; + tensor var_11841_pad_0 = const()[name = tensor("op_11841_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1725818176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727047040))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727047232)))]; + tensor var_11841_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_11839, groups = var_6865, pad = var_11841_pad_0, pad_type = var_11841_pad_type_0, strides = var_11837, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16_palettized, x = input_683_cast_fp16)[name = tensor("op_11841_cast_fp16")]; + tensor inputs_363_cast_fp16 = add(x = var_11841_cast_fp16, y = inputs_361_cast_fp16)[name = tensor("inputs_363_cast_fp16")]; + tensor hidden_states_473_axes_0 = const()[name = tensor("hidden_states_473_axes_0"), val = tensor([1])]; + tensor hidden_states_473_gamma_0_to_fp16 = const()[name = tensor("hidden_states_473_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727049856)))]; + tensor hidden_states_473_beta_0_to_fp16 = const()[name = tensor("hidden_states_473_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727052480)))]; + tensor var_11851_to_fp16 = const()[name = tensor("op_11851_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_473_cast_fp16 = layer_norm(axes = hidden_states_473_axes_0, beta = hidden_states_473_beta_0_to_fp16, epsilon = var_11851_to_fp16, gamma = hidden_states_473_gamma_0_to_fp16, x = inputs_363_cast_fp16)[name = tensor("hidden_states_473_cast_fp16")]; + tensor var_11866 = const()[name = tensor("op_11866"), val = tensor([1, 1])]; + tensor var_11868 = const()[name = tensor("op_11868"), val = tensor([1, 1])]; + tensor q_243_pad_type_0 = const()[name = tensor("q_243_pad_type_0"), val = tensor("custom")]; + tensor q_243_pad_0 = const()[name = tensor("q_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727055104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728283968))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_243_cast_fp16 = conv(dilations = var_11868, groups = var_6865, pad = q_243_pad_0, pad_type = q_243_pad_type_0, strides = var_11866, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_473_cast_fp16)[name = tensor("q_243_cast_fp16")]; + tensor var_11872 = const()[name = tensor("op_11872"), val = tensor([1, 1])]; + tensor var_11874 = const()[name = tensor("op_11874"), val = tensor([1, 1])]; + tensor k_243_pad_type_0 = const()[name = tensor("k_243_pad_type_0"), val = tensor("custom")]; + tensor k_243_pad_0 = const()[name = tensor("k_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728284160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1730250304))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_243_cast_fp16 = conv(dilations = var_11874, groups = var_6865, pad = k_243_pad_0, pad_type = k_243_pad_type_0, strides = var_11872, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_243_cast_fp16")]; + tensor var_11878 = const()[name = tensor("op_11878"), val = tensor([1, 1])]; + tensor var_11880 = const()[name = tensor("op_11880"), val = tensor([1, 1])]; + tensor v_243_pad_type_0 = const()[name = tensor("v_243_pad_type_0"), val = tensor("custom")]; + tensor v_243_pad_0 = const()[name = tensor("v_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1730250496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1732216640))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_243_cast_fp16 = conv(dilations = var_11880, groups = var_6865, pad = v_243_pad_0, pad_type = v_243_pad_type_0, strides = var_11878, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_243_cast_fp16")]; + tensor var_11884 = const()[name = tensor("op_11884"), val = tensor([1, 20, 64, -1])]; + tensor var_11885_cast_fp16 = reshape(shape = var_11884, x = q_243_cast_fp16)[name = tensor("op_11885_cast_fp16")]; + tensor var_11886 = const()[name = tensor("op_11886"), val = tensor([1, 20, 64, -1])]; + tensor var_11887_cast_fp16 = reshape(shape = var_11886, x = k_243_cast_fp16)[name = tensor("op_11887_cast_fp16")]; + tensor var_11888 = const()[name = tensor("op_11888"), val = tensor([1, 20, 64, -1])]; + tensor var_11889_cast_fp16 = reshape(shape = var_11888, x = v_243_cast_fp16)[name = tensor("op_11889_cast_fp16")]; + tensor attn_weights_485_transpose_x_0 = const()[name = tensor("attn_weights_485_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_485_transpose_y_0 = const()[name = tensor("attn_weights_485_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_485_cast_fp16 = matmul(transpose_x = attn_weights_485_transpose_x_0, transpose_y = attn_weights_485_transpose_y_0, x = var_11885_cast_fp16, y = var_11887_cast_fp16)[name = tensor("attn_weights_485_cast_fp16")]; + tensor attn_weights_487_cast_fp16 = mul(x = attn_weights_485_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_487_cast_fp16")]; + tensor var_11893_cast_fp16 = softmax(axis = var_6849, x = attn_weights_487_cast_fp16)[name = tensor("op_11893_cast_fp16")]; + tensor attn_243_transpose_x_0 = const()[name = tensor("attn_243_transpose_x_0"), val = tensor(false)]; + tensor attn_243_transpose_y_0 = const()[name = tensor("attn_243_transpose_y_0"), val = tensor(true)]; + tensor attn_243_cast_fp16 = matmul(transpose_x = attn_243_transpose_x_0, transpose_y = attn_243_transpose_y_0, x = var_11889_cast_fp16, y = var_11893_cast_fp16)[name = tensor("attn_243_cast_fp16")]; + tensor var_11897 = const()[name = tensor("op_11897"), val = tensor([1, 1280, 1, -1])]; + tensor input_685_cast_fp16 = reshape(shape = var_11897, x = attn_243_cast_fp16)[name = tensor("input_685_cast_fp16")]; + tensor var_11902 = const()[name = tensor("op_11902"), val = tensor([1, 1])]; + tensor var_11904 = const()[name = tensor("op_11904"), val = tensor([1, 1])]; + tensor var_11906_pad_type_0 = const()[name = tensor("op_11906_pad_type_0"), val = tensor("custom")]; + tensor var_11906_pad_0 = const()[name = tensor("op_11906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1732216832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733445696))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733445888)))]; + tensor var_11906_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_11904, groups = var_6865, pad = var_11906_pad_0, pad_type = var_11906_pad_type_0, strides = var_11902, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16_palettized, x = input_685_cast_fp16)[name = tensor("op_11906_cast_fp16")]; + tensor inputs_365_cast_fp16 = add(x = var_11906_cast_fp16, y = inputs_363_cast_fp16)[name = tensor("inputs_365_cast_fp16")]; + tensor input_687_axes_0 = const()[name = tensor("input_687_axes_0"), val = tensor([1])]; + tensor input_687_gamma_0_to_fp16 = const()[name = tensor("input_687_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733448512)))]; + tensor input_687_beta_0_to_fp16 = const()[name = tensor("input_687_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733451136)))]; + tensor var_11916_to_fp16 = const()[name = tensor("op_11916_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_687_cast_fp16 = layer_norm(axes = input_687_axes_0, beta = input_687_beta_0_to_fp16, epsilon = var_11916_to_fp16, gamma = input_687_gamma_0_to_fp16, x = inputs_365_cast_fp16)[name = tensor("input_687_cast_fp16")]; + tensor var_11932 = const()[name = tensor("op_11932"), val = tensor([1, 1])]; + tensor var_11934 = const()[name = tensor("op_11934"), val = tensor([1, 1])]; + tensor var_11936_pad_type_0 = const()[name = tensor("op_11936_pad_type_0"), val = tensor("custom")]; + tensor var_11936_pad_0 = const()[name = tensor("op_11936_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1733453760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743284224))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743284416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743292160))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_11936_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_11934, groups = var_6865, pad = var_11936_pad_0, pad_type = var_11936_pad_type_0, strides = var_11932, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16_palettized, x = input_687_cast_fp16)[name = tensor("op_11936_cast_fp16")]; + tensor var_11937_split_sizes_0 = const()[name = tensor("op_11937_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11937_axis_0 = const()[name = tensor("op_11937_axis_0"), val = tensor(1)]; + tensor var_11937_cast_fp16_0, tensor var_11937_cast_fp16_1 = split(axis = var_11937_axis_0, split_sizes = var_11937_split_sizes_0, x = var_11936_cast_fp16)[name = tensor("op_11937_cast_fp16")]; + tensor var_11939_mode_0 = const()[name = tensor("op_11939_mode_0"), val = tensor("EXACT")]; + tensor var_11939_cast_fp16 = gelu(mode = var_11939_mode_0, x = var_11937_cast_fp16_1)[name = tensor("op_11939_cast_fp16")]; + tensor input_689_cast_fp16 = mul(x = var_11937_cast_fp16_0, y = var_11939_cast_fp16)[name = tensor("input_689_cast_fp16")]; + tensor var_11943 = const()[name = tensor("op_11943"), val = tensor([1, 1])]; + tensor var_11945 = const()[name = tensor("op_11945"), val = tensor([1, 1])]; + tensor var_11947_pad_type_0 = const()[name = tensor("op_11947_pad_type_0"), val = tensor("custom")]; + tensor var_11947_pad_0 = const()[name = tensor("op_11947_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1743292352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748207616))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748207808)))]; + tensor var_11947_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_11945, groups = var_6865, pad = var_11947_pad_0, pad_type = var_11947_pad_type_0, strides = var_11943, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16_palettized, x = input_689_cast_fp16)[name = tensor("op_11947_cast_fp16")]; + tensor inputs_367_cast_fp16 = add(x = var_11947_cast_fp16, y = inputs_365_cast_fp16)[name = tensor("inputs_367_cast_fp16")]; + tensor hidden_states_477_axes_0 = const()[name = tensor("hidden_states_477_axes_0"), val = tensor([1])]; + tensor hidden_states_477_gamma_0_to_fp16 = const()[name = tensor("hidden_states_477_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748210432)))]; + tensor hidden_states_477_beta_0_to_fp16 = const()[name = tensor("hidden_states_477_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748213056)))]; + tensor var_11963_to_fp16 = const()[name = tensor("op_11963_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_477_cast_fp16 = layer_norm(axes = hidden_states_477_axes_0, beta = hidden_states_477_beta_0_to_fp16, epsilon = var_11963_to_fp16, gamma = hidden_states_477_gamma_0_to_fp16, x = inputs_367_cast_fp16)[name = tensor("hidden_states_477_cast_fp16")]; + tensor var_11978 = const()[name = tensor("op_11978"), val = tensor([1, 1])]; + tensor var_11980 = const()[name = tensor("op_11980"), val = tensor([1, 1])]; + tensor q_245_pad_type_0 = const()[name = tensor("q_245_pad_type_0"), val = tensor("custom")]; + tensor q_245_pad_0 = const()[name = tensor("q_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1748215680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1749444544))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_245_cast_fp16 = conv(dilations = var_11980, groups = var_6865, pad = q_245_pad_0, pad_type = q_245_pad_type_0, strides = var_11978, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_477_cast_fp16)[name = tensor("q_245_cast_fp16")]; + tensor var_11984 = const()[name = tensor("op_11984"), val = tensor([1, 1])]; + tensor var_11986 = const()[name = tensor("op_11986"), val = tensor([1, 1])]; + tensor k_245_pad_type_0 = const()[name = tensor("k_245_pad_type_0"), val = tensor("custom")]; + tensor k_245_pad_0 = const()[name = tensor("k_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1749444736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1750673600))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_245_cast_fp16 = conv(dilations = var_11986, groups = var_6865, pad = k_245_pad_0, pad_type = k_245_pad_type_0, strides = var_11984, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_477_cast_fp16)[name = tensor("k_245_cast_fp16")]; + tensor var_11990 = const()[name = tensor("op_11990"), val = tensor([1, 1])]; + tensor var_11992 = const()[name = tensor("op_11992"), val = tensor([1, 1])]; + tensor v_245_pad_type_0 = const()[name = tensor("v_245_pad_type_0"), val = tensor("custom")]; + tensor v_245_pad_0 = const()[name = tensor("v_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1750673792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1751902656))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_245_cast_fp16 = conv(dilations = var_11992, groups = var_6865, pad = v_245_pad_0, pad_type = v_245_pad_type_0, strides = var_11990, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_477_cast_fp16)[name = tensor("v_245_cast_fp16")]; + tensor var_11996 = const()[name = tensor("op_11996"), val = tensor([1, 20, 64, -1])]; + tensor var_11997_cast_fp16 = reshape(shape = var_11996, x = q_245_cast_fp16)[name = tensor("op_11997_cast_fp16")]; + tensor var_11998 = const()[name = tensor("op_11998"), val = tensor([1, 20, 64, -1])]; + tensor var_11999_cast_fp16 = reshape(shape = var_11998, x = k_245_cast_fp16)[name = tensor("op_11999_cast_fp16")]; + tensor var_12000 = const()[name = tensor("op_12000"), val = tensor([1, 20, 64, -1])]; + tensor var_12001_cast_fp16 = reshape(shape = var_12000, x = v_245_cast_fp16)[name = tensor("op_12001_cast_fp16")]; + tensor attn_weights_489_transpose_x_0 = const()[name = tensor("attn_weights_489_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_489_transpose_y_0 = const()[name = tensor("attn_weights_489_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_489_cast_fp16 = matmul(transpose_x = attn_weights_489_transpose_x_0, transpose_y = attn_weights_489_transpose_y_0, x = var_11997_cast_fp16, y = var_11999_cast_fp16)[name = tensor("attn_weights_489_cast_fp16")]; + tensor attn_weights_491_cast_fp16 = mul(x = attn_weights_489_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_491_cast_fp16")]; + tensor var_12005_cast_fp16 = softmax(axis = var_6849, x = attn_weights_491_cast_fp16)[name = tensor("op_12005_cast_fp16")]; + tensor attn_245_transpose_x_0 = const()[name = tensor("attn_245_transpose_x_0"), val = tensor(false)]; + tensor attn_245_transpose_y_0 = const()[name = tensor("attn_245_transpose_y_0"), val = tensor(true)]; + tensor attn_245_cast_fp16 = matmul(transpose_x = attn_245_transpose_x_0, transpose_y = attn_245_transpose_y_0, x = var_12001_cast_fp16, y = var_12005_cast_fp16)[name = tensor("attn_245_cast_fp16")]; + tensor var_12009 = const()[name = tensor("op_12009"), val = tensor([1, 1280, 1, -1])]; + tensor input_691_cast_fp16 = reshape(shape = var_12009, x = attn_245_cast_fp16)[name = tensor("input_691_cast_fp16")]; + tensor var_12014 = const()[name = tensor("op_12014"), val = tensor([1, 1])]; + tensor var_12016 = const()[name = tensor("op_12016"), val = tensor([1, 1])]; + tensor var_12018_pad_type_0 = const()[name = tensor("op_12018_pad_type_0"), val = tensor("custom")]; + tensor var_12018_pad_0 = const()[name = tensor("op_12018_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1751902848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753131712))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753131904)))]; + tensor var_12018_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_12016, groups = var_6865, pad = var_12018_pad_0, pad_type = var_12018_pad_type_0, strides = var_12014, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16_palettized, x = input_691_cast_fp16)[name = tensor("op_12018_cast_fp16")]; + tensor inputs_369_cast_fp16 = add(x = var_12018_cast_fp16, y = inputs_367_cast_fp16)[name = tensor("inputs_369_cast_fp16")]; + tensor hidden_states_479_axes_0 = const()[name = tensor("hidden_states_479_axes_0"), val = tensor([1])]; + tensor hidden_states_479_gamma_0_to_fp16 = const()[name = tensor("hidden_states_479_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753134528)))]; + tensor hidden_states_479_beta_0_to_fp16 = const()[name = tensor("hidden_states_479_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753137152)))]; + tensor var_12028_to_fp16 = const()[name = tensor("op_12028_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_479_cast_fp16 = layer_norm(axes = hidden_states_479_axes_0, beta = hidden_states_479_beta_0_to_fp16, epsilon = var_12028_to_fp16, gamma = hidden_states_479_gamma_0_to_fp16, x = inputs_369_cast_fp16)[name = tensor("hidden_states_479_cast_fp16")]; + tensor var_12043 = const()[name = tensor("op_12043"), val = tensor([1, 1])]; + tensor var_12045 = const()[name = tensor("op_12045"), val = tensor([1, 1])]; + tensor q_247_pad_type_0 = const()[name = tensor("q_247_pad_type_0"), val = tensor("custom")]; + tensor q_247_pad_0 = const()[name = tensor("q_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1753139776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1754368640))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_247_cast_fp16 = conv(dilations = var_12045, groups = var_6865, pad = q_247_pad_0, pad_type = q_247_pad_type_0, strides = var_12043, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_479_cast_fp16)[name = tensor("q_247_cast_fp16")]; + tensor var_12049 = const()[name = tensor("op_12049"), val = tensor([1, 1])]; + tensor var_12051 = const()[name = tensor("op_12051"), val = tensor([1, 1])]; + tensor k_247_pad_type_0 = const()[name = tensor("k_247_pad_type_0"), val = tensor("custom")]; + tensor k_247_pad_0 = const()[name = tensor("k_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1754368832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756334976))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_247_cast_fp16 = conv(dilations = var_12051, groups = var_6865, pad = k_247_pad_0, pad_type = k_247_pad_type_0, strides = var_12049, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_247_cast_fp16")]; + tensor var_12055 = const()[name = tensor("op_12055"), val = tensor([1, 1])]; + tensor var_12057 = const()[name = tensor("op_12057"), val = tensor([1, 1])]; + tensor v_247_pad_type_0 = const()[name = tensor("v_247_pad_type_0"), val = tensor("custom")]; + tensor v_247_pad_0 = const()[name = tensor("v_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756335168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1758301312))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_247_cast_fp16 = conv(dilations = var_12057, groups = var_6865, pad = v_247_pad_0, pad_type = v_247_pad_type_0, strides = var_12055, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_247_cast_fp16")]; + tensor var_12061 = const()[name = tensor("op_12061"), val = tensor([1, 20, 64, -1])]; + tensor var_12062_cast_fp16 = reshape(shape = var_12061, x = q_247_cast_fp16)[name = tensor("op_12062_cast_fp16")]; + tensor var_12063 = const()[name = tensor("op_12063"), val = tensor([1, 20, 64, -1])]; + tensor var_12064_cast_fp16 = reshape(shape = var_12063, x = k_247_cast_fp16)[name = tensor("op_12064_cast_fp16")]; + tensor var_12065 = const()[name = tensor("op_12065"), val = tensor([1, 20, 64, -1])]; + tensor var_12066_cast_fp16 = reshape(shape = var_12065, x = v_247_cast_fp16)[name = tensor("op_12066_cast_fp16")]; + tensor attn_weights_493_transpose_x_0 = const()[name = tensor("attn_weights_493_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_493_transpose_y_0 = const()[name = tensor("attn_weights_493_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_493_cast_fp16 = matmul(transpose_x = attn_weights_493_transpose_x_0, transpose_y = attn_weights_493_transpose_y_0, x = var_12062_cast_fp16, y = var_12064_cast_fp16)[name = tensor("attn_weights_493_cast_fp16")]; + tensor attn_weights_495_cast_fp16 = mul(x = attn_weights_493_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_495_cast_fp16")]; + tensor var_12070_cast_fp16 = softmax(axis = var_6849, x = attn_weights_495_cast_fp16)[name = tensor("op_12070_cast_fp16")]; + tensor attn_247_transpose_x_0 = const()[name = tensor("attn_247_transpose_x_0"), val = tensor(false)]; + tensor attn_247_transpose_y_0 = const()[name = tensor("attn_247_transpose_y_0"), val = tensor(true)]; + tensor attn_247_cast_fp16 = matmul(transpose_x = attn_247_transpose_x_0, transpose_y = attn_247_transpose_y_0, x = var_12066_cast_fp16, y = var_12070_cast_fp16)[name = tensor("attn_247_cast_fp16")]; + tensor var_12074 = const()[name = tensor("op_12074"), val = tensor([1, 1280, 1, -1])]; + tensor input_693_cast_fp16 = reshape(shape = var_12074, x = attn_247_cast_fp16)[name = tensor("input_693_cast_fp16")]; + tensor var_12079 = const()[name = tensor("op_12079"), val = tensor([1, 1])]; + tensor var_12081 = const()[name = tensor("op_12081"), val = tensor([1, 1])]; + tensor var_12083_pad_type_0 = const()[name = tensor("op_12083_pad_type_0"), val = tensor("custom")]; + tensor var_12083_pad_0 = const()[name = tensor("op_12083_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1758301504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759530368))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759530560)))]; + tensor var_12083_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_12081, groups = var_6865, pad = var_12083_pad_0, pad_type = var_12083_pad_type_0, strides = var_12079, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16_palettized, x = input_693_cast_fp16)[name = tensor("op_12083_cast_fp16")]; + tensor inputs_371_cast_fp16 = add(x = var_12083_cast_fp16, y = inputs_369_cast_fp16)[name = tensor("inputs_371_cast_fp16")]; + tensor input_695_axes_0 = const()[name = tensor("input_695_axes_0"), val = tensor([1])]; + tensor input_695_gamma_0_to_fp16 = const()[name = tensor("input_695_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759533184)))]; + tensor input_695_beta_0_to_fp16 = const()[name = tensor("input_695_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759535808)))]; + tensor var_12093_to_fp16 = const()[name = tensor("op_12093_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = input_695_beta_0_to_fp16, epsilon = var_12093_to_fp16, gamma = input_695_gamma_0_to_fp16, x = inputs_371_cast_fp16)[name = tensor("input_695_cast_fp16")]; + tensor var_12109 = const()[name = tensor("op_12109"), val = tensor([1, 1])]; + tensor var_12111 = const()[name = tensor("op_12111"), val = tensor([1, 1])]; + tensor var_12113_pad_type_0 = const()[name = tensor("op_12113_pad_type_0"), val = tensor("custom")]; + tensor var_12113_pad_0 = const()[name = tensor("op_12113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1759538432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769368896))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769369088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769376832))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_12113_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12111, groups = var_6865, pad = var_12113_pad_0, pad_type = var_12113_pad_type_0, strides = var_12109, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16_palettized, x = input_695_cast_fp16)[name = tensor("op_12113_cast_fp16")]; + tensor var_12114_split_sizes_0 = const()[name = tensor("op_12114_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12114_axis_0 = const()[name = tensor("op_12114_axis_0"), val = tensor(1)]; + tensor var_12114_cast_fp16_0, tensor var_12114_cast_fp16_1 = split(axis = var_12114_axis_0, split_sizes = var_12114_split_sizes_0, x = var_12113_cast_fp16)[name = tensor("op_12114_cast_fp16")]; + tensor var_12116_mode_0 = const()[name = tensor("op_12116_mode_0"), val = tensor("EXACT")]; + tensor var_12116_cast_fp16 = gelu(mode = var_12116_mode_0, x = var_12114_cast_fp16_1)[name = tensor("op_12116_cast_fp16")]; + tensor input_697_cast_fp16 = mul(x = var_12114_cast_fp16_0, y = var_12116_cast_fp16)[name = tensor("input_697_cast_fp16")]; + tensor var_12120 = const()[name = tensor("op_12120"), val = tensor([1, 1])]; + tensor var_12122 = const()[name = tensor("op_12122"), val = tensor([1, 1])]; + tensor var_12124_pad_type_0 = const()[name = tensor("op_12124_pad_type_0"), val = tensor("custom")]; + tensor var_12124_pad_0 = const()[name = tensor("op_12124_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1769377024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774292288))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774292480)))]; + tensor var_12124_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_12122, groups = var_6865, pad = var_12124_pad_0, pad_type = var_12124_pad_type_0, strides = var_12120, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16_palettized, x = input_697_cast_fp16)[name = tensor("op_12124_cast_fp16")]; + tensor inputs_373_cast_fp16 = add(x = var_12124_cast_fp16, y = inputs_371_cast_fp16)[name = tensor("inputs_373_cast_fp16")]; + tensor hidden_states_483_axes_0 = const()[name = tensor("hidden_states_483_axes_0"), val = tensor([1])]; + tensor hidden_states_483_gamma_0_to_fp16 = const()[name = tensor("hidden_states_483_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774295104)))]; + tensor hidden_states_483_beta_0_to_fp16 = const()[name = tensor("hidden_states_483_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774297728)))]; + tensor var_12140_to_fp16 = const()[name = tensor("op_12140_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_483_cast_fp16 = layer_norm(axes = hidden_states_483_axes_0, beta = hidden_states_483_beta_0_to_fp16, epsilon = var_12140_to_fp16, gamma = hidden_states_483_gamma_0_to_fp16, x = inputs_373_cast_fp16)[name = tensor("hidden_states_483_cast_fp16")]; + tensor var_12155 = const()[name = tensor("op_12155"), val = tensor([1, 1])]; + tensor var_12157 = const()[name = tensor("op_12157"), val = tensor([1, 1])]; + tensor q_249_pad_type_0 = const()[name = tensor("q_249_pad_type_0"), val = tensor("custom")]; + tensor q_249_pad_0 = const()[name = tensor("q_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1774300352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1775529216))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_249_cast_fp16 = conv(dilations = var_12157, groups = var_6865, pad = q_249_pad_0, pad_type = q_249_pad_type_0, strides = var_12155, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_483_cast_fp16)[name = tensor("q_249_cast_fp16")]; + tensor var_12161 = const()[name = tensor("op_12161"), val = tensor([1, 1])]; + tensor var_12163 = const()[name = tensor("op_12163"), val = tensor([1, 1])]; + tensor k_249_pad_type_0 = const()[name = tensor("k_249_pad_type_0"), val = tensor("custom")]; + tensor k_249_pad_0 = const()[name = tensor("k_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1775529408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1776758272))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_249_cast_fp16 = conv(dilations = var_12163, groups = var_6865, pad = k_249_pad_0, pad_type = k_249_pad_type_0, strides = var_12161, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_483_cast_fp16)[name = tensor("k_249_cast_fp16")]; + tensor var_12167 = const()[name = tensor("op_12167"), val = tensor([1, 1])]; + tensor var_12169 = const()[name = tensor("op_12169"), val = tensor([1, 1])]; + tensor v_249_pad_type_0 = const()[name = tensor("v_249_pad_type_0"), val = tensor("custom")]; + tensor v_249_pad_0 = const()[name = tensor("v_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1776758464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1777987328))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_249_cast_fp16 = conv(dilations = var_12169, groups = var_6865, pad = v_249_pad_0, pad_type = v_249_pad_type_0, strides = var_12167, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_483_cast_fp16)[name = tensor("v_249_cast_fp16")]; + tensor var_12173 = const()[name = tensor("op_12173"), val = tensor([1, 20, 64, -1])]; + tensor var_12174_cast_fp16 = reshape(shape = var_12173, x = q_249_cast_fp16)[name = tensor("op_12174_cast_fp16")]; + tensor var_12175 = const()[name = tensor("op_12175"), val = tensor([1, 20, 64, -1])]; + tensor var_12176_cast_fp16 = reshape(shape = var_12175, x = k_249_cast_fp16)[name = tensor("op_12176_cast_fp16")]; + tensor var_12177 = const()[name = tensor("op_12177"), val = tensor([1, 20, 64, -1])]; + tensor var_12178_cast_fp16 = reshape(shape = var_12177, x = v_249_cast_fp16)[name = tensor("op_12178_cast_fp16")]; + tensor attn_weights_497_transpose_x_0 = const()[name = tensor("attn_weights_497_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_497_transpose_y_0 = const()[name = tensor("attn_weights_497_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_497_cast_fp16 = matmul(transpose_x = attn_weights_497_transpose_x_0, transpose_y = attn_weights_497_transpose_y_0, x = var_12174_cast_fp16, y = var_12176_cast_fp16)[name = tensor("attn_weights_497_cast_fp16")]; + tensor attn_weights_499_cast_fp16 = mul(x = attn_weights_497_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_499_cast_fp16")]; + tensor var_12182_cast_fp16 = softmax(axis = var_6849, x = attn_weights_499_cast_fp16)[name = tensor("op_12182_cast_fp16")]; + tensor attn_249_transpose_x_0 = const()[name = tensor("attn_249_transpose_x_0"), val = tensor(false)]; + tensor attn_249_transpose_y_0 = const()[name = tensor("attn_249_transpose_y_0"), val = tensor(true)]; + tensor attn_249_cast_fp16 = matmul(transpose_x = attn_249_transpose_x_0, transpose_y = attn_249_transpose_y_0, x = var_12178_cast_fp16, y = var_12182_cast_fp16)[name = tensor("attn_249_cast_fp16")]; + tensor var_12186 = const()[name = tensor("op_12186"), val = tensor([1, 1280, 1, -1])]; + tensor input_699_cast_fp16 = reshape(shape = var_12186, x = attn_249_cast_fp16)[name = tensor("input_699_cast_fp16")]; + tensor var_12191 = const()[name = tensor("op_12191"), val = tensor([1, 1])]; + tensor var_12193 = const()[name = tensor("op_12193"), val = tensor([1, 1])]; + tensor var_12195_pad_type_0 = const()[name = tensor("op_12195_pad_type_0"), val = tensor("custom")]; + tensor var_12195_pad_0 = const()[name = tensor("op_12195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1777987520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779216384))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779216576)))]; + tensor var_12195_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_12193, groups = var_6865, pad = var_12195_pad_0, pad_type = var_12195_pad_type_0, strides = var_12191, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16_palettized, x = input_699_cast_fp16)[name = tensor("op_12195_cast_fp16")]; + tensor inputs_375_cast_fp16 = add(x = var_12195_cast_fp16, y = inputs_373_cast_fp16)[name = tensor("inputs_375_cast_fp16")]; + tensor hidden_states_485_axes_0 = const()[name = tensor("hidden_states_485_axes_0"), val = tensor([1])]; + tensor hidden_states_485_gamma_0_to_fp16 = const()[name = tensor("hidden_states_485_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779219200)))]; + tensor hidden_states_485_beta_0_to_fp16 = const()[name = tensor("hidden_states_485_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779221824)))]; + tensor var_12205_to_fp16 = const()[name = tensor("op_12205_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_485_cast_fp16 = layer_norm(axes = hidden_states_485_axes_0, beta = hidden_states_485_beta_0_to_fp16, epsilon = var_12205_to_fp16, gamma = hidden_states_485_gamma_0_to_fp16, x = inputs_375_cast_fp16)[name = tensor("hidden_states_485_cast_fp16")]; + tensor var_12220 = const()[name = tensor("op_12220"), val = tensor([1, 1])]; + tensor var_12222 = const()[name = tensor("op_12222"), val = tensor([1, 1])]; + tensor q_251_pad_type_0 = const()[name = tensor("q_251_pad_type_0"), val = tensor("custom")]; + tensor q_251_pad_0 = const()[name = tensor("q_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779224448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1780453312))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_251_cast_fp16 = conv(dilations = var_12222, groups = var_6865, pad = q_251_pad_0, pad_type = q_251_pad_type_0, strides = var_12220, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_485_cast_fp16)[name = tensor("q_251_cast_fp16")]; + tensor var_12226 = const()[name = tensor("op_12226"), val = tensor([1, 1])]; + tensor var_12228 = const()[name = tensor("op_12228"), val = tensor([1, 1])]; + tensor k_251_pad_type_0 = const()[name = tensor("k_251_pad_type_0"), val = tensor("custom")]; + tensor k_251_pad_0 = const()[name = tensor("k_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1780453504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782419648))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_251_cast_fp16 = conv(dilations = var_12228, groups = var_6865, pad = k_251_pad_0, pad_type = k_251_pad_type_0, strides = var_12226, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_251_cast_fp16")]; + tensor var_12232 = const()[name = tensor("op_12232"), val = tensor([1, 1])]; + tensor var_12234 = const()[name = tensor("op_12234"), val = tensor([1, 1])]; + tensor v_251_pad_type_0 = const()[name = tensor("v_251_pad_type_0"), val = tensor("custom")]; + tensor v_251_pad_0 = const()[name = tensor("v_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782419840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1784385984))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_251_cast_fp16 = conv(dilations = var_12234, groups = var_6865, pad = v_251_pad_0, pad_type = v_251_pad_type_0, strides = var_12232, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_251_cast_fp16")]; + tensor var_12238 = const()[name = tensor("op_12238"), val = tensor([1, 20, 64, -1])]; + tensor var_12239_cast_fp16 = reshape(shape = var_12238, x = q_251_cast_fp16)[name = tensor("op_12239_cast_fp16")]; + tensor var_12240 = const()[name = tensor("op_12240"), val = tensor([1, 20, 64, -1])]; + tensor var_12241_cast_fp16 = reshape(shape = var_12240, x = k_251_cast_fp16)[name = tensor("op_12241_cast_fp16")]; + tensor var_12242 = const()[name = tensor("op_12242"), val = tensor([1, 20, 64, -1])]; + tensor var_12243_cast_fp16 = reshape(shape = var_12242, x = v_251_cast_fp16)[name = tensor("op_12243_cast_fp16")]; + tensor attn_weights_501_transpose_x_0 = const()[name = tensor("attn_weights_501_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_501_transpose_y_0 = const()[name = tensor("attn_weights_501_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_501_cast_fp16 = matmul(transpose_x = attn_weights_501_transpose_x_0, transpose_y = attn_weights_501_transpose_y_0, x = var_12239_cast_fp16, y = var_12241_cast_fp16)[name = tensor("attn_weights_501_cast_fp16")]; + tensor attn_weights_503_cast_fp16 = mul(x = attn_weights_501_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_503_cast_fp16")]; + tensor var_12247_cast_fp16 = softmax(axis = var_6849, x = attn_weights_503_cast_fp16)[name = tensor("op_12247_cast_fp16")]; + tensor attn_251_transpose_x_0 = const()[name = tensor("attn_251_transpose_x_0"), val = tensor(false)]; + tensor attn_251_transpose_y_0 = const()[name = tensor("attn_251_transpose_y_0"), val = tensor(true)]; + tensor attn_251_cast_fp16 = matmul(transpose_x = attn_251_transpose_x_0, transpose_y = attn_251_transpose_y_0, x = var_12243_cast_fp16, y = var_12247_cast_fp16)[name = tensor("attn_251_cast_fp16")]; + tensor var_12251 = const()[name = tensor("op_12251"), val = tensor([1, 1280, 1, -1])]; + tensor input_701_cast_fp16 = reshape(shape = var_12251, x = attn_251_cast_fp16)[name = tensor("input_701_cast_fp16")]; + tensor var_12256 = const()[name = tensor("op_12256"), val = tensor([1, 1])]; + tensor var_12258 = const()[name = tensor("op_12258"), val = tensor([1, 1])]; + tensor var_12260_pad_type_0 = const()[name = tensor("op_12260_pad_type_0"), val = tensor("custom")]; + tensor var_12260_pad_0 = const()[name = tensor("op_12260_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1784386176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785615040))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785615232)))]; + tensor var_12260_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_12258, groups = var_6865, pad = var_12260_pad_0, pad_type = var_12260_pad_type_0, strides = var_12256, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16_palettized, x = input_701_cast_fp16)[name = tensor("op_12260_cast_fp16")]; + tensor inputs_377_cast_fp16 = add(x = var_12260_cast_fp16, y = inputs_375_cast_fp16)[name = tensor("inputs_377_cast_fp16")]; + tensor input_703_axes_0 = const()[name = tensor("input_703_axes_0"), val = tensor([1])]; + tensor input_703_gamma_0_to_fp16 = const()[name = tensor("input_703_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785617856)))]; + tensor input_703_beta_0_to_fp16 = const()[name = tensor("input_703_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785620480)))]; + tensor var_12270_to_fp16 = const()[name = tensor("op_12270_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = input_703_beta_0_to_fp16, epsilon = var_12270_to_fp16, gamma = input_703_gamma_0_to_fp16, x = inputs_377_cast_fp16)[name = tensor("input_703_cast_fp16")]; + tensor var_12286 = const()[name = tensor("op_12286"), val = tensor([1, 1])]; + tensor var_12288 = const()[name = tensor("op_12288"), val = tensor([1, 1])]; + tensor var_12290_pad_type_0 = const()[name = tensor("op_12290_pad_type_0"), val = tensor("custom")]; + tensor var_12290_pad_0 = const()[name = tensor("op_12290_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1785623104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795453568))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795453760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795461504))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_12290_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12288, groups = var_6865, pad = var_12290_pad_0, pad_type = var_12290_pad_type_0, strides = var_12286, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16_palettized, x = input_703_cast_fp16)[name = tensor("op_12290_cast_fp16")]; + tensor var_12291_split_sizes_0 = const()[name = tensor("op_12291_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12291_axis_0 = const()[name = tensor("op_12291_axis_0"), val = tensor(1)]; + tensor var_12291_cast_fp16_0, tensor var_12291_cast_fp16_1 = split(axis = var_12291_axis_0, split_sizes = var_12291_split_sizes_0, x = var_12290_cast_fp16)[name = tensor("op_12291_cast_fp16")]; + tensor var_12293_mode_0 = const()[name = tensor("op_12293_mode_0"), val = tensor("EXACT")]; + tensor var_12293_cast_fp16 = gelu(mode = var_12293_mode_0, x = var_12291_cast_fp16_1)[name = tensor("op_12293_cast_fp16")]; + tensor input_705_cast_fp16 = mul(x = var_12291_cast_fp16_0, y = var_12293_cast_fp16)[name = tensor("input_705_cast_fp16")]; + tensor var_12297 = const()[name = tensor("op_12297"), val = tensor([1, 1])]; + tensor var_12299 = const()[name = tensor("op_12299"), val = tensor([1, 1])]; + tensor var_12301_pad_type_0 = const()[name = tensor("op_12301_pad_type_0"), val = tensor("custom")]; + tensor var_12301_pad_0 = const()[name = tensor("op_12301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795461696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800376960))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800377152)))]; + tensor var_12301_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_12299, groups = var_6865, pad = var_12301_pad_0, pad_type = var_12301_pad_type_0, strides = var_12297, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16_palettized, x = input_705_cast_fp16)[name = tensor("op_12301_cast_fp16")]; + tensor inputs_379_cast_fp16 = add(x = var_12301_cast_fp16, y = inputs_377_cast_fp16)[name = tensor("inputs_379_cast_fp16")]; + tensor hidden_states_489_axes_0 = const()[name = tensor("hidden_states_489_axes_0"), val = tensor([1])]; + tensor hidden_states_489_gamma_0_to_fp16 = const()[name = tensor("hidden_states_489_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800379776)))]; + tensor hidden_states_489_beta_0_to_fp16 = const()[name = tensor("hidden_states_489_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800382400)))]; + tensor var_12317_to_fp16 = const()[name = tensor("op_12317_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_489_cast_fp16 = layer_norm(axes = hidden_states_489_axes_0, beta = hidden_states_489_beta_0_to_fp16, epsilon = var_12317_to_fp16, gamma = hidden_states_489_gamma_0_to_fp16, x = inputs_379_cast_fp16)[name = tensor("hidden_states_489_cast_fp16")]; + tensor var_12332 = const()[name = tensor("op_12332"), val = tensor([1, 1])]; + tensor var_12334 = const()[name = tensor("op_12334"), val = tensor([1, 1])]; + tensor q_253_pad_type_0 = const()[name = tensor("q_253_pad_type_0"), val = tensor("custom")]; + tensor q_253_pad_0 = const()[name = tensor("q_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1800385024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1801613888))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_253_cast_fp16 = conv(dilations = var_12334, groups = var_6865, pad = q_253_pad_0, pad_type = q_253_pad_type_0, strides = var_12332, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_489_cast_fp16)[name = tensor("q_253_cast_fp16")]; + tensor var_12338 = const()[name = tensor("op_12338"), val = tensor([1, 1])]; + tensor var_12340 = const()[name = tensor("op_12340"), val = tensor([1, 1])]; + tensor k_253_pad_type_0 = const()[name = tensor("k_253_pad_type_0"), val = tensor("custom")]; + tensor k_253_pad_0 = const()[name = tensor("k_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1801614080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1802842944))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_253_cast_fp16 = conv(dilations = var_12340, groups = var_6865, pad = k_253_pad_0, pad_type = k_253_pad_type_0, strides = var_12338, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_489_cast_fp16)[name = tensor("k_253_cast_fp16")]; + tensor var_12344 = const()[name = tensor("op_12344"), val = tensor([1, 1])]; + tensor var_12346 = const()[name = tensor("op_12346"), val = tensor([1, 1])]; + tensor v_253_pad_type_0 = const()[name = tensor("v_253_pad_type_0"), val = tensor("custom")]; + tensor v_253_pad_0 = const()[name = tensor("v_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1802843136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1804072000))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_253_cast_fp16 = conv(dilations = var_12346, groups = var_6865, pad = v_253_pad_0, pad_type = v_253_pad_type_0, strides = var_12344, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_489_cast_fp16)[name = tensor("v_253_cast_fp16")]; + tensor var_12350 = const()[name = tensor("op_12350"), val = tensor([1, 20, 64, -1])]; + tensor var_12351_cast_fp16 = reshape(shape = var_12350, x = q_253_cast_fp16)[name = tensor("op_12351_cast_fp16")]; + tensor var_12352 = const()[name = tensor("op_12352"), val = tensor([1, 20, 64, -1])]; + tensor var_12353_cast_fp16 = reshape(shape = var_12352, x = k_253_cast_fp16)[name = tensor("op_12353_cast_fp16")]; + tensor var_12354 = const()[name = tensor("op_12354"), val = tensor([1, 20, 64, -1])]; + tensor var_12355_cast_fp16 = reshape(shape = var_12354, x = v_253_cast_fp16)[name = tensor("op_12355_cast_fp16")]; + tensor attn_weights_505_transpose_x_0 = const()[name = tensor("attn_weights_505_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_505_transpose_y_0 = const()[name = tensor("attn_weights_505_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_505_cast_fp16 = matmul(transpose_x = attn_weights_505_transpose_x_0, transpose_y = attn_weights_505_transpose_y_0, x = var_12351_cast_fp16, y = var_12353_cast_fp16)[name = tensor("attn_weights_505_cast_fp16")]; + tensor attn_weights_507_cast_fp16 = mul(x = attn_weights_505_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_507_cast_fp16")]; + tensor var_12359_cast_fp16 = softmax(axis = var_6849, x = attn_weights_507_cast_fp16)[name = tensor("op_12359_cast_fp16")]; + tensor attn_253_transpose_x_0 = const()[name = tensor("attn_253_transpose_x_0"), val = tensor(false)]; + tensor attn_253_transpose_y_0 = const()[name = tensor("attn_253_transpose_y_0"), val = tensor(true)]; + tensor attn_253_cast_fp16 = matmul(transpose_x = attn_253_transpose_x_0, transpose_y = attn_253_transpose_y_0, x = var_12355_cast_fp16, y = var_12359_cast_fp16)[name = tensor("attn_253_cast_fp16")]; + tensor var_12363 = const()[name = tensor("op_12363"), val = tensor([1, 1280, 1, -1])]; + tensor input_707_cast_fp16 = reshape(shape = var_12363, x = attn_253_cast_fp16)[name = tensor("input_707_cast_fp16")]; + tensor var_12368 = const()[name = tensor("op_12368"), val = tensor([1, 1])]; + tensor var_12370 = const()[name = tensor("op_12370"), val = tensor([1, 1])]; + tensor var_12372_pad_type_0 = const()[name = tensor("op_12372_pad_type_0"), val = tensor("custom")]; + tensor var_12372_pad_0 = const()[name = tensor("op_12372_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1804072192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805301056))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805301248)))]; + tensor var_12372_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_12370, groups = var_6865, pad = var_12372_pad_0, pad_type = var_12372_pad_type_0, strides = var_12368, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16_palettized, x = input_707_cast_fp16)[name = tensor("op_12372_cast_fp16")]; + tensor inputs_381_cast_fp16 = add(x = var_12372_cast_fp16, y = inputs_379_cast_fp16)[name = tensor("inputs_381_cast_fp16")]; + tensor hidden_states_491_axes_0 = const()[name = tensor("hidden_states_491_axes_0"), val = tensor([1])]; + tensor hidden_states_491_gamma_0_to_fp16 = const()[name = tensor("hidden_states_491_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805303872)))]; + tensor hidden_states_491_beta_0_to_fp16 = const()[name = tensor("hidden_states_491_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805306496)))]; + tensor var_12382_to_fp16 = const()[name = tensor("op_12382_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_491_cast_fp16 = layer_norm(axes = hidden_states_491_axes_0, beta = hidden_states_491_beta_0_to_fp16, epsilon = var_12382_to_fp16, gamma = hidden_states_491_gamma_0_to_fp16, x = inputs_381_cast_fp16)[name = tensor("hidden_states_491_cast_fp16")]; + tensor var_12397 = const()[name = tensor("op_12397"), val = tensor([1, 1])]; + tensor var_12399 = const()[name = tensor("op_12399"), val = tensor([1, 1])]; + tensor q_255_pad_type_0 = const()[name = tensor("q_255_pad_type_0"), val = tensor("custom")]; + tensor q_255_pad_0 = const()[name = tensor("q_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805309120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1806537984))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_255_cast_fp16 = conv(dilations = var_12399, groups = var_6865, pad = q_255_pad_0, pad_type = q_255_pad_type_0, strides = var_12397, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_491_cast_fp16)[name = tensor("q_255_cast_fp16")]; + tensor var_12403 = const()[name = tensor("op_12403"), val = tensor([1, 1])]; + tensor var_12405 = const()[name = tensor("op_12405"), val = tensor([1, 1])]; + tensor k_255_pad_type_0 = const()[name = tensor("k_255_pad_type_0"), val = tensor("custom")]; + tensor k_255_pad_0 = const()[name = tensor("k_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1806538176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808504320))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor k_255_cast_fp16 = conv(dilations = var_12405, groups = var_6865, pad = k_255_pad_0, pad_type = k_255_pad_type_0, strides = var_12403, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_255_cast_fp16")]; + tensor var_12409 = const()[name = tensor("op_12409"), val = tensor([1, 1])]; + tensor var_12411 = const()[name = tensor("op_12411"), val = tensor([1, 1])]; + tensor v_255_pad_type_0 = const()[name = tensor("v_255_pad_type_0"), val = tensor("custom")]; + tensor v_255_pad_0 = const()[name = tensor("v_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808504512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810470656))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 2048, 1, 1])]; + tensor v_255_cast_fp16 = conv(dilations = var_12411, groups = var_6865, pad = v_255_pad_0, pad_type = v_255_pad_type_0, strides = var_12409, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_255_cast_fp16")]; + tensor var_12415 = const()[name = tensor("op_12415"), val = tensor([1, 20, 64, -1])]; + tensor var_12416_cast_fp16 = reshape(shape = var_12415, x = q_255_cast_fp16)[name = tensor("op_12416_cast_fp16")]; + tensor var_12417 = const()[name = tensor("op_12417"), val = tensor([1, 20, 64, -1])]; + tensor var_12418_cast_fp16 = reshape(shape = var_12417, x = k_255_cast_fp16)[name = tensor("op_12418_cast_fp16")]; + tensor var_12419 = const()[name = tensor("op_12419"), val = tensor([1, 20, 64, -1])]; + tensor var_12420_cast_fp16 = reshape(shape = var_12419, x = v_255_cast_fp16)[name = tensor("op_12420_cast_fp16")]; + tensor attn_weights_509_transpose_x_0 = const()[name = tensor("attn_weights_509_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_509_transpose_y_0 = const()[name = tensor("attn_weights_509_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_509_cast_fp16 = matmul(transpose_x = attn_weights_509_transpose_x_0, transpose_y = attn_weights_509_transpose_y_0, x = var_12416_cast_fp16, y = var_12418_cast_fp16)[name = tensor("attn_weights_509_cast_fp16")]; + tensor attn_weights_511_cast_fp16 = mul(x = attn_weights_509_cast_fp16, y = var_6856_to_fp16)[name = tensor("attn_weights_511_cast_fp16")]; + tensor var_12424_cast_fp16 = softmax(axis = var_6849, x = attn_weights_511_cast_fp16)[name = tensor("op_12424_cast_fp16")]; + tensor attn_255_transpose_x_0 = const()[name = tensor("attn_255_transpose_x_0"), val = tensor(false)]; + tensor attn_255_transpose_y_0 = const()[name = tensor("attn_255_transpose_y_0"), val = tensor(true)]; + tensor attn_255_cast_fp16 = matmul(transpose_x = attn_255_transpose_x_0, transpose_y = attn_255_transpose_y_0, x = var_12420_cast_fp16, y = var_12424_cast_fp16)[name = tensor("attn_255_cast_fp16")]; + tensor var_12428 = const()[name = tensor("op_12428"), val = tensor([1, 1280, 1, -1])]; + tensor input_709_cast_fp16 = reshape(shape = var_12428, x = attn_255_cast_fp16)[name = tensor("input_709_cast_fp16")]; + tensor var_12433 = const()[name = tensor("op_12433"), val = tensor([1, 1])]; + tensor var_12435 = const()[name = tensor("op_12435"), val = tensor([1, 1])]; + tensor var_12437_pad_type_0 = const()[name = tensor("op_12437_pad_type_0"), val = tensor("custom")]; + tensor var_12437_pad_0 = const()[name = tensor("op_12437_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1810470848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811699712))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811699904)))]; + tensor var_12437_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_12435, groups = var_6865, pad = var_12437_pad_0, pad_type = var_12437_pad_type_0, strides = var_12433, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16_palettized, x = input_709_cast_fp16)[name = tensor("op_12437_cast_fp16")]; + tensor inputs_383_cast_fp16 = add(x = var_12437_cast_fp16, y = inputs_381_cast_fp16)[name = tensor("inputs_383_cast_fp16")]; + tensor input_711_axes_0 = const()[name = tensor("input_711_axes_0"), val = tensor([1])]; + tensor input_711_gamma_0_to_fp16 = const()[name = tensor("input_711_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811702528)))]; + tensor input_711_beta_0_to_fp16 = const()[name = tensor("input_711_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811705152)))]; + tensor var_12447_to_fp16 = const()[name = tensor("op_12447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_711_cast_fp16 = layer_norm(axes = input_711_axes_0, beta = input_711_beta_0_to_fp16, epsilon = var_12447_to_fp16, gamma = input_711_gamma_0_to_fp16, x = inputs_383_cast_fp16)[name = tensor("input_711_cast_fp16")]; + tensor var_12463 = const()[name = tensor("op_12463"), val = tensor([1, 1])]; + tensor var_12465 = const()[name = tensor("op_12465"), val = tensor([1, 1])]; + tensor var_12467_pad_type_0 = const()[name = tensor("op_12467_pad_type_0"), val = tensor("custom")]; + tensor var_12467_pad_0 = const()[name = tensor("op_12467_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1811707776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821538240))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821538432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821546176))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_12467_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12465, groups = var_6865, pad = var_12467_pad_0, pad_type = var_12467_pad_type_0, strides = var_12463, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16_palettized, x = input_711_cast_fp16)[name = tensor("op_12467_cast_fp16")]; + tensor var_12468_split_sizes_0 = const()[name = tensor("op_12468_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12468_axis_0 = const()[name = tensor("op_12468_axis_0"), val = tensor(1)]; + tensor var_12468_cast_fp16_0, tensor var_12468_cast_fp16_1 = split(axis = var_12468_axis_0, split_sizes = var_12468_split_sizes_0, x = var_12467_cast_fp16)[name = tensor("op_12468_cast_fp16")]; + tensor var_12470_mode_0 = const()[name = tensor("op_12470_mode_0"), val = tensor("EXACT")]; + tensor var_12470_cast_fp16 = gelu(mode = var_12470_mode_0, x = var_12468_cast_fp16_1)[name = tensor("op_12470_cast_fp16")]; + tensor input_713_cast_fp16 = mul(x = var_12468_cast_fp16_0, y = var_12470_cast_fp16)[name = tensor("input_713_cast_fp16")]; + tensor var_12474 = const()[name = tensor("op_12474"), val = tensor([1, 1])]; + tensor var_12476 = const()[name = tensor("op_12476"), val = tensor([1, 1])]; + tensor var_12478_pad_type_0 = const()[name = tensor("op_12478_pad_type_0"), val = tensor("custom")]; + tensor var_12478_pad_0 = const()[name = tensor("op_12478_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1821546368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1826461632))), name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1826461824)))]; + tensor var_12478_cast_fp16 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_12476, groups = var_6865, pad = var_12478_pad_0, pad_type = var_12478_pad_type_0, strides = var_12474, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16_palettized, x = input_713_cast_fp16)[name = tensor("op_12478_cast_fp16")]; + tensor hidden_states_495_cast_fp16 = add(x = var_12478_cast_fp16, y = inputs_383_cast_fp16)[name = tensor("hidden_states_495_cast_fp16")]; + tensor var_12480 = const()[name = tensor("op_12480"), val = tensor([1, 1280, 32, 32])]; + tensor input_715_cast_fp16 = reshape(shape = var_12480, x = hidden_states_495_cast_fp16)[name = tensor("input_715_cast_fp16")]; + tensor var_12484 = const()[name = tensor("op_12484"), val = tensor([1, 1])]; + tensor var_12486 = const()[name = tensor("op_12486"), val = tensor([1, 1])]; + tensor hidden_states_497_pad_type_0 = const()[name = tensor("hidden_states_497_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_497_pad_0 = const()[name = tensor("hidden_states_497_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1826464448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1827693312))), name = tensor("up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1827693504)))]; + tensor hidden_states_497_cast_fp16 = conv(bias = up_blocks_0_attentions_2_proj_out_bias_to_fp16, dilations = var_12486, groups = var_6865, pad = hidden_states_497_pad_0, pad_type = hidden_states_497_pad_type_0, strides = var_12484, weight = up_blocks_0_attentions_2_proj_out_weight_to_fp16_palettized, x = input_715_cast_fp16)[name = tensor("hidden_states_497_cast_fp16")]; + tensor input_717_cast_fp16 = add(x = hidden_states_497_cast_fp16, y = hidden_states_431_cast_fp16)[name = tensor("input_717_cast_fp16")]; + tensor input_719_scale_factor_height_0 = const()[name = tensor("input_719_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_719_scale_factor_width_0 = const()[name = tensor("input_719_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_719_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_719_scale_factor_height_0, scale_factor_width = input_719_scale_factor_width_0, x = input_717_cast_fp16)[name = tensor("input_719_cast_fp16")]; + tensor var_12495 = const()[name = tensor("op_12495"), val = tensor([1, 1])]; + tensor var_12497 = const()[name = tensor("op_12497"), val = tensor([1, 1])]; + tensor hidden_states_499_pad_type_0 = const()[name = tensor("hidden_states_499_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_499_pad_0 = const()[name = tensor("hidden_states_499_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1827696128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838755392))), name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838755584)))]; + tensor hidden_states_499_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_12497, groups = var_6865, pad = hidden_states_499_pad_0, pad_type = hidden_states_499_pad_type_0, strides = var_12495, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = tensor("hidden_states_499_cast_fp16")]; + tensor var_12502 = const()[name = tensor("op_12502"), val = tensor(3)]; + tensor var_12518 = const()[name = tensor("op_12518"), val = tensor(1)]; + tensor input_721_interleave_0 = const()[name = tensor("input_721_interleave_0"), val = tensor(false)]; + tensor input_721_cast_fp16 = concat(axis = var_12518, interleave = input_721_interleave_0, values = (hidden_states_499_cast_fp16, input_113_cast_fp16))[name = tensor("input_721_cast_fp16")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([1, 32, 60, 64, 64])]; + tensor reshape_120_cast_fp16 = reshape(shape = reshape_120_shape_0, x = input_721_cast_fp16)[name = tensor("reshape_120_cast_fp16")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast_fp16)[name = tensor("reduce_mean_90_cast_fp16")]; + tensor sub_60_cast_fp16 = sub(x = reshape_120_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor("sub_60_cast_fp16")]; + tensor square_30_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor("square_30_cast_fp16")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast_fp16)[name = tensor("reduce_mean_92_cast_fp16")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast_fp16 = add(x = reduce_mean_92_cast_fp16, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast_fp16")]; + tensor sqrt_30_cast_fp16 = sqrt(x = add_60_cast_fp16)[name = tensor("sqrt_30_cast_fp16")]; + tensor real_div_30_cast_fp16 = real_div(x = sub_60_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor("real_div_30_cast_fp16")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([1, 1920, 64, 64])]; + tensor reshape_121_cast_fp16 = reshape(shape = reshape_121_shape_0, x = real_div_30_cast_fp16)[name = tensor("reshape_121_cast_fp16")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838758208)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838762112)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast_fp16 = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_121_cast_fp16)[name = tensor("add_61_cast_fp16")]; + tensor input_725_cast_fp16 = silu(x = add_61_cast_fp16)[name = tensor("input_725_cast_fp16")]; + tensor var_12547 = const()[name = tensor("op_12547"), val = tensor([1, 1])]; + tensor var_12549 = const()[name = tensor("op_12549"), val = tensor([1, 1])]; + tensor hidden_states_501_pad_type_0 = const()[name = tensor("hidden_states_501_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_501_pad_0 = const()[name = tensor("hidden_states_501_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1838766016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847060480))), name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1920, 3, 3])]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847060672)))]; + tensor hidden_states_501_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_12549, groups = var_12518, pad = hidden_states_501_pad_0, pad_type = hidden_states_501_pad_type_0, strides = var_12547, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_725_cast_fp16)[name = tensor("hidden_states_501_cast_fp16")]; + tensor var_12555 = const()[name = tensor("op_12555"), val = tensor([1, 1])]; + tensor var_12557 = const()[name = tensor("op_12557"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847062016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847676480))), name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847676672)))]; + tensor temb_23_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_12557, groups = var_12518, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_12555, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_23_cast_fp16")]; + tensor input_729_cast_fp16 = add(x = hidden_states_501_cast_fp16, y = temb_23_cast_fp16)[name = tensor("input_729_cast_fp16")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_124_cast_fp16 = reshape(shape = reshape_124_shape_0, x = input_729_cast_fp16)[name = tensor("reshape_124_cast_fp16")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast_fp16)[name = tensor("reduce_mean_93_cast_fp16")]; + tensor sub_62_cast_fp16 = sub(x = reshape_124_cast_fp16, y = reduce_mean_93_cast_fp16)[name = tensor("sub_62_cast_fp16")]; + tensor square_31_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor("square_31_cast_fp16")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast_fp16)[name = tensor("reduce_mean_95_cast_fp16")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast_fp16 = add(x = reduce_mean_95_cast_fp16, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast_fp16")]; + tensor sqrt_31_cast_fp16 = sqrt(x = add_62_cast_fp16)[name = tensor("sqrt_31_cast_fp16")]; + tensor real_div_31_cast_fp16 = real_div(x = sub_62_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor("real_div_31_cast_fp16")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_125_cast_fp16 = reshape(shape = reshape_125_shape_0, x = real_div_31_cast_fp16)[name = tensor("reshape_125_cast_fp16")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847678016)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847679360)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast_fp16 = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_125_cast_fp16)[name = tensor("add_63_cast_fp16")]; + tensor input_733_cast_fp16 = silu(x = add_63_cast_fp16)[name = tensor("input_733_cast_fp16")]; + tensor var_12567 = const()[name = tensor("op_12567"), val = tensor([1, 1])]; + tensor var_12569 = const()[name = tensor("op_12569"), val = tensor([1, 1])]; + tensor hidden_states_503_pad_type_0 = const()[name = tensor("hidden_states_503_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_503_pad_0 = const()[name = tensor("hidden_states_503_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1847680704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1850445568))), name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1850445760)))]; + tensor hidden_states_503_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_12569, groups = var_12518, pad = hidden_states_503_pad_0, pad_type = hidden_states_503_pad_type_0, strides = var_12567, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_733_cast_fp16)[name = tensor("hidden_states_503_cast_fp16")]; + tensor var_12574 = const()[name = tensor("op_12574"), val = tensor([1, 1])]; + tensor var_12576 = const()[name = tensor("op_12576"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1850447104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851368768))), name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1920, 1, 1])]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851368960)))]; + tensor x_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_12576, groups = var_12518, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_12574, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_721_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor hidden_states_505_cast_fp16 = add(x = x_11_cast_fp16, y = hidden_states_503_cast_fp16)[name = tensor("hidden_states_505_cast_fp16")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_128_cast_fp16 = reshape(shape = reshape_128_shape_0, x = hidden_states_505_cast_fp16)[name = tensor("reshape_128_cast_fp16")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast_fp16 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast_fp16)[name = tensor("reduce_mean_96_cast_fp16")]; + tensor sub_64_cast_fp16 = sub(x = reshape_128_cast_fp16, y = reduce_mean_96_cast_fp16)[name = tensor("sub_64_cast_fp16")]; + tensor square_32_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor("square_32_cast_fp16")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast_fp16 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast_fp16)[name = tensor("reduce_mean_98_cast_fp16")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_64_cast_fp16 = add(x = reduce_mean_98_cast_fp16, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast_fp16")]; + tensor sqrt_32_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor("sqrt_32_cast_fp16")]; + tensor real_div_32_cast_fp16 = real_div(x = sub_64_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor("real_div_32_cast_fp16")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_129_cast_fp16 = reshape(shape = reshape_129_shape_0, x = real_div_32_cast_fp16)[name = tensor("reshape_129_cast_fp16")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851370304)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851371648)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast_fp16 = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_129_cast_fp16)[name = tensor("add_65_cast_fp16")]; + tensor var_12598 = const()[name = tensor("op_12598"), val = tensor([1, 1])]; + tensor var_12600 = const()[name = tensor("op_12600"), val = tensor([1, 1])]; + tensor hidden_states_507_pad_type_0 = const()[name = tensor("hidden_states_507_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_507_pad_0 = const()[name = tensor("hidden_states_507_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851372992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851680256))), name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851680448)))]; + tensor hidden_states_507_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_12600, groups = var_12518, pad = hidden_states_507_pad_0, pad_type = hidden_states_507_pad_type_0, strides = var_12598, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_65_cast_fp16)[name = tensor("hidden_states_507_cast_fp16")]; + tensor var_12605 = const()[name = tensor("op_12605"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_385_cast_fp16 = reshape(shape = var_12605, x = hidden_states_507_cast_fp16)[name = tensor("inputs_385_cast_fp16")]; + tensor hidden_states_509_axes_0 = const()[name = tensor("hidden_states_509_axes_0"), val = tensor([1])]; + tensor hidden_states_509_gamma_0_to_fp16 = const()[name = tensor("hidden_states_509_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851681792)))]; + tensor hidden_states_509_beta_0_to_fp16 = const()[name = tensor("hidden_states_509_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851683136)))]; + tensor var_12621_to_fp16 = const()[name = tensor("op_12621_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_509_cast_fp16 = layer_norm(axes = hidden_states_509_axes_0, beta = hidden_states_509_beta_0_to_fp16, epsilon = var_12621_to_fp16, gamma = hidden_states_509_gamma_0_to_fp16, x = inputs_385_cast_fp16)[name = tensor("hidden_states_509_cast_fp16")]; + tensor var_12636 = const()[name = tensor("op_12636"), val = tensor([1, 1])]; + tensor var_12638 = const()[name = tensor("op_12638"), val = tensor([1, 1])]; + tensor q_257_pad_type_0 = const()[name = tensor("q_257_pad_type_0"), val = tensor("custom")]; + tensor q_257_pad_0 = const()[name = tensor("q_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851684480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851991744))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_257_cast_fp16 = conv(dilations = var_12638, groups = var_12518, pad = q_257_pad_0, pad_type = q_257_pad_type_0, strides = var_12636, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_509_cast_fp16)[name = tensor("q_257_cast_fp16")]; + tensor var_12642 = const()[name = tensor("op_12642"), val = tensor([1, 1])]; + tensor var_12644 = const()[name = tensor("op_12644"), val = tensor([1, 1])]; + tensor k_257_pad_type_0 = const()[name = tensor("k_257_pad_type_0"), val = tensor("custom")]; + tensor k_257_pad_0 = const()[name = tensor("k_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1851991936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852299200))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_257_cast_fp16 = conv(dilations = var_12644, groups = var_12518, pad = k_257_pad_0, pad_type = k_257_pad_type_0, strides = var_12642, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_509_cast_fp16)[name = tensor("k_257_cast_fp16")]; + tensor var_12648 = const()[name = tensor("op_12648"), val = tensor([1, 1])]; + tensor var_12650 = const()[name = tensor("op_12650"), val = tensor([1, 1])]; + tensor v_257_pad_type_0 = const()[name = tensor("v_257_pad_type_0"), val = tensor("custom")]; + tensor v_257_pad_0 = const()[name = tensor("v_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852299392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852606656))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_257_cast_fp16 = conv(dilations = var_12650, groups = var_12518, pad = v_257_pad_0, pad_type = v_257_pad_type_0, strides = var_12648, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_509_cast_fp16)[name = tensor("v_257_cast_fp16")]; + tensor var_12654 = const()[name = tensor("op_12654"), val = tensor([1, 10, 64, -1])]; + tensor var_12655_cast_fp16 = reshape(shape = var_12654, x = q_257_cast_fp16)[name = tensor("op_12655_cast_fp16")]; + tensor var_12656 = const()[name = tensor("op_12656"), val = tensor([1, 10, 64, -1])]; + tensor var_12657_cast_fp16 = reshape(shape = var_12656, x = k_257_cast_fp16)[name = tensor("op_12657_cast_fp16")]; + tensor var_12658 = const()[name = tensor("op_12658"), val = tensor([1, 10, 64, -1])]; + tensor var_12659_cast_fp16 = reshape(shape = var_12658, x = v_257_cast_fp16)[name = tensor("op_12659_cast_fp16")]; + tensor attn_weights_513_transpose_x_0 = const()[name = tensor("attn_weights_513_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_513_transpose_y_0 = const()[name = tensor("attn_weights_513_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_513_cast_fp16 = matmul(transpose_x = attn_weights_513_transpose_x_0, transpose_y = attn_weights_513_transpose_y_0, x = var_12655_cast_fp16, y = var_12657_cast_fp16)[name = tensor("attn_weights_513_cast_fp16")]; + tensor var_12509_to_fp16 = const()[name = tensor("op_12509_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_515_cast_fp16 = mul(x = attn_weights_513_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_515_cast_fp16")]; + tensor var_12663_cast_fp16 = softmax(axis = var_12502, x = attn_weights_515_cast_fp16)[name = tensor("op_12663_cast_fp16")]; + tensor attn_257_transpose_x_0 = const()[name = tensor("attn_257_transpose_x_0"), val = tensor(false)]; + tensor attn_257_transpose_y_0 = const()[name = tensor("attn_257_transpose_y_0"), val = tensor(true)]; + tensor attn_257_cast_fp16 = matmul(transpose_x = attn_257_transpose_x_0, transpose_y = attn_257_transpose_y_0, x = var_12659_cast_fp16, y = var_12663_cast_fp16)[name = tensor("attn_257_cast_fp16")]; + tensor var_12667 = const()[name = tensor("op_12667"), val = tensor([1, 640, 1, -1])]; + tensor input_737_cast_fp16 = reshape(shape = var_12667, x = attn_257_cast_fp16)[name = tensor("input_737_cast_fp16")]; + tensor var_12672 = const()[name = tensor("op_12672"), val = tensor([1, 1])]; + tensor var_12674 = const()[name = tensor("op_12674"), val = tensor([1, 1])]; + tensor var_12676_pad_type_0 = const()[name = tensor("op_12676_pad_type_0"), val = tensor("custom")]; + tensor var_12676_pad_0 = const()[name = tensor("op_12676_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852606848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852914112))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852914304)))]; + tensor var_12676_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_12674, groups = var_12518, pad = var_12676_pad_0, pad_type = var_12676_pad_type_0, strides = var_12672, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_737_cast_fp16)[name = tensor("op_12676_cast_fp16")]; + tensor inputs_387_cast_fp16 = add(x = var_12676_cast_fp16, y = inputs_385_cast_fp16)[name = tensor("inputs_387_cast_fp16")]; + tensor hidden_states_511_axes_0 = const()[name = tensor("hidden_states_511_axes_0"), val = tensor([1])]; + tensor hidden_states_511_gamma_0_to_fp16 = const()[name = tensor("hidden_states_511_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852915648)))]; + tensor hidden_states_511_beta_0_to_fp16 = const()[name = tensor("hidden_states_511_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852916992)))]; + tensor var_12686_to_fp16 = const()[name = tensor("op_12686_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_511_cast_fp16 = layer_norm(axes = hidden_states_511_axes_0, beta = hidden_states_511_beta_0_to_fp16, epsilon = var_12686_to_fp16, gamma = hidden_states_511_gamma_0_to_fp16, x = inputs_387_cast_fp16)[name = tensor("hidden_states_511_cast_fp16")]; + tensor var_12701 = const()[name = tensor("op_12701"), val = tensor([1, 1])]; + tensor var_12703 = const()[name = tensor("op_12703"), val = tensor([1, 1])]; + tensor q_259_pad_type_0 = const()[name = tensor("q_259_pad_type_0"), val = tensor("custom")]; + tensor q_259_pad_0 = const()[name = tensor("q_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852918336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1853225600))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_259_cast_fp16 = conv(dilations = var_12703, groups = var_12518, pad = q_259_pad_0, pad_type = q_259_pad_type_0, strides = var_12701, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_511_cast_fp16)[name = tensor("q_259_cast_fp16")]; + tensor var_12707 = const()[name = tensor("op_12707"), val = tensor([1, 1])]; + tensor var_12709 = const()[name = tensor("op_12709"), val = tensor([1, 1])]; + tensor k_259_pad_type_0 = const()[name = tensor("k_259_pad_type_0"), val = tensor("custom")]; + tensor k_259_pad_0 = const()[name = tensor("k_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1853225792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1854208896))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_259_cast_fp16 = conv(dilations = var_12709, groups = var_12518, pad = k_259_pad_0, pad_type = k_259_pad_type_0, strides = var_12707, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_259_cast_fp16")]; + tensor var_12713 = const()[name = tensor("op_12713"), val = tensor([1, 1])]; + tensor var_12715 = const()[name = tensor("op_12715"), val = tensor([1, 1])]; + tensor v_259_pad_type_0 = const()[name = tensor("v_259_pad_type_0"), val = tensor("custom")]; + tensor v_259_pad_0 = const()[name = tensor("v_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1854209088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855192192))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_259_cast_fp16 = conv(dilations = var_12715, groups = var_12518, pad = v_259_pad_0, pad_type = v_259_pad_type_0, strides = var_12713, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_259_cast_fp16")]; + tensor var_12719 = const()[name = tensor("op_12719"), val = tensor([1, 10, 64, -1])]; + tensor var_12720_cast_fp16 = reshape(shape = var_12719, x = q_259_cast_fp16)[name = tensor("op_12720_cast_fp16")]; + tensor var_12721 = const()[name = tensor("op_12721"), val = tensor([1, 10, 64, -1])]; + tensor var_12722_cast_fp16 = reshape(shape = var_12721, x = k_259_cast_fp16)[name = tensor("op_12722_cast_fp16")]; + tensor var_12723 = const()[name = tensor("op_12723"), val = tensor([1, 10, 64, -1])]; + tensor var_12724_cast_fp16 = reshape(shape = var_12723, x = v_259_cast_fp16)[name = tensor("op_12724_cast_fp16")]; + tensor attn_weights_517_transpose_x_0 = const()[name = tensor("attn_weights_517_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_517_transpose_y_0 = const()[name = tensor("attn_weights_517_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_517_cast_fp16 = matmul(transpose_x = attn_weights_517_transpose_x_0, transpose_y = attn_weights_517_transpose_y_0, x = var_12720_cast_fp16, y = var_12722_cast_fp16)[name = tensor("attn_weights_517_cast_fp16")]; + tensor attn_weights_519_cast_fp16 = mul(x = attn_weights_517_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_519_cast_fp16")]; + tensor var_12728_cast_fp16 = softmax(axis = var_12502, x = attn_weights_519_cast_fp16)[name = tensor("op_12728_cast_fp16")]; + tensor attn_259_transpose_x_0 = const()[name = tensor("attn_259_transpose_x_0"), val = tensor(false)]; + tensor attn_259_transpose_y_0 = const()[name = tensor("attn_259_transpose_y_0"), val = tensor(true)]; + tensor attn_259_cast_fp16 = matmul(transpose_x = attn_259_transpose_x_0, transpose_y = attn_259_transpose_y_0, x = var_12724_cast_fp16, y = var_12728_cast_fp16)[name = tensor("attn_259_cast_fp16")]; + tensor var_12732 = const()[name = tensor("op_12732"), val = tensor([1, 640, 1, -1])]; + tensor input_739_cast_fp16 = reshape(shape = var_12732, x = attn_259_cast_fp16)[name = tensor("input_739_cast_fp16")]; + tensor var_12737 = const()[name = tensor("op_12737"), val = tensor([1, 1])]; + tensor var_12739 = const()[name = tensor("op_12739"), val = tensor([1, 1])]; + tensor var_12741_pad_type_0 = const()[name = tensor("op_12741_pad_type_0"), val = tensor("custom")]; + tensor var_12741_pad_0 = const()[name = tensor("op_12741_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855192384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855499648))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855499840)))]; + tensor var_12741_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_12739, groups = var_12518, pad = var_12741_pad_0, pad_type = var_12741_pad_type_0, strides = var_12737, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_739_cast_fp16)[name = tensor("op_12741_cast_fp16")]; + tensor inputs_389_cast_fp16 = add(x = var_12741_cast_fp16, y = inputs_387_cast_fp16)[name = tensor("inputs_389_cast_fp16")]; + tensor input_741_axes_0 = const()[name = tensor("input_741_axes_0"), val = tensor([1])]; + tensor input_741_gamma_0_to_fp16 = const()[name = tensor("input_741_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855501184)))]; + tensor input_741_beta_0_to_fp16 = const()[name = tensor("input_741_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855502528)))]; + tensor var_12751_to_fp16 = const()[name = tensor("op_12751_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_741_cast_fp16 = layer_norm(axes = input_741_axes_0, beta = input_741_beta_0_to_fp16, epsilon = var_12751_to_fp16, gamma = input_741_gamma_0_to_fp16, x = inputs_389_cast_fp16)[name = tensor("input_741_cast_fp16")]; + tensor var_12767 = const()[name = tensor("op_12767"), val = tensor([1, 1])]; + tensor var_12769 = const()[name = tensor("op_12769"), val = tensor([1, 1])]; + tensor var_12771_pad_type_0 = const()[name = tensor("op_12771_pad_type_0"), val = tensor("custom")]; + tensor var_12771_pad_0 = const()[name = tensor("op_12771_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1855503872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1857961536))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1857961728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1857965632))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_12771_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12769, groups = var_12518, pad = var_12771_pad_0, pad_type = var_12771_pad_type_0, strides = var_12767, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_741_cast_fp16)[name = tensor("op_12771_cast_fp16")]; + tensor var_12772_split_sizes_0 = const()[name = tensor("op_12772_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12772_axis_0 = const()[name = tensor("op_12772_axis_0"), val = tensor(1)]; + tensor var_12772_cast_fp16_0, tensor var_12772_cast_fp16_1 = split(axis = var_12772_axis_0, split_sizes = var_12772_split_sizes_0, x = var_12771_cast_fp16)[name = tensor("op_12772_cast_fp16")]; + tensor var_12774_mode_0 = const()[name = tensor("op_12774_mode_0"), val = tensor("EXACT")]; + tensor var_12774_cast_fp16 = gelu(mode = var_12774_mode_0, x = var_12772_cast_fp16_1)[name = tensor("op_12774_cast_fp16")]; + tensor input_743_cast_fp16 = mul(x = var_12772_cast_fp16_0, y = var_12774_cast_fp16)[name = tensor("input_743_cast_fp16")]; + tensor var_12778 = const()[name = tensor("op_12778"), val = tensor([1, 1])]; + tensor var_12780 = const()[name = tensor("op_12780"), val = tensor([1, 1])]; + tensor var_12782_pad_type_0 = const()[name = tensor("op_12782_pad_type_0"), val = tensor("custom")]; + tensor var_12782_pad_0 = const()[name = tensor("op_12782_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1857965824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859194688))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859194880)))]; + tensor var_12782_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_12780, groups = var_12518, pad = var_12782_pad_0, pad_type = var_12782_pad_type_0, strides = var_12778, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_743_cast_fp16)[name = tensor("op_12782_cast_fp16")]; + tensor inputs_391_cast_fp16 = add(x = var_12782_cast_fp16, y = inputs_389_cast_fp16)[name = tensor("inputs_391_cast_fp16")]; + tensor hidden_states_515_axes_0 = const()[name = tensor("hidden_states_515_axes_0"), val = tensor([1])]; + tensor hidden_states_515_gamma_0_to_fp16 = const()[name = tensor("hidden_states_515_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859196224)))]; + tensor hidden_states_515_beta_0_to_fp16 = const()[name = tensor("hidden_states_515_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859197568)))]; + tensor var_12798_to_fp16 = const()[name = tensor("op_12798_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_515_cast_fp16 = layer_norm(axes = hidden_states_515_axes_0, beta = hidden_states_515_beta_0_to_fp16, epsilon = var_12798_to_fp16, gamma = hidden_states_515_gamma_0_to_fp16, x = inputs_391_cast_fp16)[name = tensor("hidden_states_515_cast_fp16")]; + tensor var_12813 = const()[name = tensor("op_12813"), val = tensor([1, 1])]; + tensor var_12815 = const()[name = tensor("op_12815"), val = tensor([1, 1])]; + tensor q_261_pad_type_0 = const()[name = tensor("q_261_pad_type_0"), val = tensor("custom")]; + tensor q_261_pad_0 = const()[name = tensor("q_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859198912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859506176))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_261_cast_fp16 = conv(dilations = var_12815, groups = var_12518, pad = q_261_pad_0, pad_type = q_261_pad_type_0, strides = var_12813, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_515_cast_fp16)[name = tensor("q_261_cast_fp16")]; + tensor var_12819 = const()[name = tensor("op_12819"), val = tensor([1, 1])]; + tensor var_12821 = const()[name = tensor("op_12821"), val = tensor([1, 1])]; + tensor k_261_pad_type_0 = const()[name = tensor("k_261_pad_type_0"), val = tensor("custom")]; + tensor k_261_pad_0 = const()[name = tensor("k_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859506368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859813632))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_261_cast_fp16 = conv(dilations = var_12821, groups = var_12518, pad = k_261_pad_0, pad_type = k_261_pad_type_0, strides = var_12819, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_515_cast_fp16)[name = tensor("k_261_cast_fp16")]; + tensor var_12825 = const()[name = tensor("op_12825"), val = tensor([1, 1])]; + tensor var_12827 = const()[name = tensor("op_12827"), val = tensor([1, 1])]; + tensor v_261_pad_type_0 = const()[name = tensor("v_261_pad_type_0"), val = tensor("custom")]; + tensor v_261_pad_0 = const()[name = tensor("v_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1859813824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860121088))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_261_cast_fp16 = conv(dilations = var_12827, groups = var_12518, pad = v_261_pad_0, pad_type = v_261_pad_type_0, strides = var_12825, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_515_cast_fp16)[name = tensor("v_261_cast_fp16")]; + tensor var_12831 = const()[name = tensor("op_12831"), val = tensor([1, 10, 64, -1])]; + tensor var_12832_cast_fp16 = reshape(shape = var_12831, x = q_261_cast_fp16)[name = tensor("op_12832_cast_fp16")]; + tensor var_12833 = const()[name = tensor("op_12833"), val = tensor([1, 10, 64, -1])]; + tensor var_12834_cast_fp16 = reshape(shape = var_12833, x = k_261_cast_fp16)[name = tensor("op_12834_cast_fp16")]; + tensor var_12835 = const()[name = tensor("op_12835"), val = tensor([1, 10, 64, -1])]; + tensor var_12836_cast_fp16 = reshape(shape = var_12835, x = v_261_cast_fp16)[name = tensor("op_12836_cast_fp16")]; + tensor attn_weights_521_transpose_x_0 = const()[name = tensor("attn_weights_521_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_521_transpose_y_0 = const()[name = tensor("attn_weights_521_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_521_cast_fp16 = matmul(transpose_x = attn_weights_521_transpose_x_0, transpose_y = attn_weights_521_transpose_y_0, x = var_12832_cast_fp16, y = var_12834_cast_fp16)[name = tensor("attn_weights_521_cast_fp16")]; + tensor attn_weights_523_cast_fp16 = mul(x = attn_weights_521_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_523_cast_fp16")]; + tensor var_12840_cast_fp16 = softmax(axis = var_12502, x = attn_weights_523_cast_fp16)[name = tensor("op_12840_cast_fp16")]; + tensor attn_261_transpose_x_0 = const()[name = tensor("attn_261_transpose_x_0"), val = tensor(false)]; + tensor attn_261_transpose_y_0 = const()[name = tensor("attn_261_transpose_y_0"), val = tensor(true)]; + tensor attn_261_cast_fp16 = matmul(transpose_x = attn_261_transpose_x_0, transpose_y = attn_261_transpose_y_0, x = var_12836_cast_fp16, y = var_12840_cast_fp16)[name = tensor("attn_261_cast_fp16")]; + tensor var_12844 = const()[name = tensor("op_12844"), val = tensor([1, 640, 1, -1])]; + tensor input_745_cast_fp16 = reshape(shape = var_12844, x = attn_261_cast_fp16)[name = tensor("input_745_cast_fp16")]; + tensor var_12849 = const()[name = tensor("op_12849"), val = tensor([1, 1])]; + tensor var_12851 = const()[name = tensor("op_12851"), val = tensor([1, 1])]; + tensor var_12853_pad_type_0 = const()[name = tensor("op_12853_pad_type_0"), val = tensor("custom")]; + tensor var_12853_pad_0 = const()[name = tensor("op_12853_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860121280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860428544))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860428736)))]; + tensor var_12853_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_12851, groups = var_12518, pad = var_12853_pad_0, pad_type = var_12853_pad_type_0, strides = var_12849, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_745_cast_fp16)[name = tensor("op_12853_cast_fp16")]; + tensor inputs_393_cast_fp16 = add(x = var_12853_cast_fp16, y = inputs_391_cast_fp16)[name = tensor("inputs_393_cast_fp16")]; + tensor hidden_states_517_axes_0 = const()[name = tensor("hidden_states_517_axes_0"), val = tensor([1])]; + tensor hidden_states_517_gamma_0_to_fp16 = const()[name = tensor("hidden_states_517_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860430080)))]; + tensor hidden_states_517_beta_0_to_fp16 = const()[name = tensor("hidden_states_517_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860431424)))]; + tensor var_12863_to_fp16 = const()[name = tensor("op_12863_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_517_cast_fp16 = layer_norm(axes = hidden_states_517_axes_0, beta = hidden_states_517_beta_0_to_fp16, epsilon = var_12863_to_fp16, gamma = hidden_states_517_gamma_0_to_fp16, x = inputs_393_cast_fp16)[name = tensor("hidden_states_517_cast_fp16")]; + tensor var_12878 = const()[name = tensor("op_12878"), val = tensor([1, 1])]; + tensor var_12880 = const()[name = tensor("op_12880"), val = tensor([1, 1])]; + tensor q_263_pad_type_0 = const()[name = tensor("q_263_pad_type_0"), val = tensor("custom")]; + tensor q_263_pad_0 = const()[name = tensor("q_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860432768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860740032))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_263_cast_fp16 = conv(dilations = var_12880, groups = var_12518, pad = q_263_pad_0, pad_type = q_263_pad_type_0, strides = var_12878, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_517_cast_fp16)[name = tensor("q_263_cast_fp16")]; + tensor var_12884 = const()[name = tensor("op_12884"), val = tensor([1, 1])]; + tensor var_12886 = const()[name = tensor("op_12886"), val = tensor([1, 1])]; + tensor k_263_pad_type_0 = const()[name = tensor("k_263_pad_type_0"), val = tensor("custom")]; + tensor k_263_pad_0 = const()[name = tensor("k_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1860740224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1861723328))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_263_cast_fp16 = conv(dilations = var_12886, groups = var_12518, pad = k_263_pad_0, pad_type = k_263_pad_type_0, strides = var_12884, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_263_cast_fp16")]; + tensor var_12890 = const()[name = tensor("op_12890"), val = tensor([1, 1])]; + tensor var_12892 = const()[name = tensor("op_12892"), val = tensor([1, 1])]; + tensor v_263_pad_type_0 = const()[name = tensor("v_263_pad_type_0"), val = tensor("custom")]; + tensor v_263_pad_0 = const()[name = tensor("v_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1861723520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1862706624))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_263_cast_fp16 = conv(dilations = var_12892, groups = var_12518, pad = v_263_pad_0, pad_type = v_263_pad_type_0, strides = var_12890, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_263_cast_fp16")]; + tensor var_12896 = const()[name = tensor("op_12896"), val = tensor([1, 10, 64, -1])]; + tensor var_12897_cast_fp16 = reshape(shape = var_12896, x = q_263_cast_fp16)[name = tensor("op_12897_cast_fp16")]; + tensor var_12898 = const()[name = tensor("op_12898"), val = tensor([1, 10, 64, -1])]; + tensor var_12899_cast_fp16 = reshape(shape = var_12898, x = k_263_cast_fp16)[name = tensor("op_12899_cast_fp16")]; + tensor var_12900 = const()[name = tensor("op_12900"), val = tensor([1, 10, 64, -1])]; + tensor var_12901_cast_fp16 = reshape(shape = var_12900, x = v_263_cast_fp16)[name = tensor("op_12901_cast_fp16")]; + tensor attn_weights_525_transpose_x_0 = const()[name = tensor("attn_weights_525_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_525_transpose_y_0 = const()[name = tensor("attn_weights_525_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_525_cast_fp16 = matmul(transpose_x = attn_weights_525_transpose_x_0, transpose_y = attn_weights_525_transpose_y_0, x = var_12897_cast_fp16, y = var_12899_cast_fp16)[name = tensor("attn_weights_525_cast_fp16")]; + tensor attn_weights_527_cast_fp16 = mul(x = attn_weights_525_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_527_cast_fp16")]; + tensor var_12905_cast_fp16 = softmax(axis = var_12502, x = attn_weights_527_cast_fp16)[name = tensor("op_12905_cast_fp16")]; + tensor attn_263_transpose_x_0 = const()[name = tensor("attn_263_transpose_x_0"), val = tensor(false)]; + tensor attn_263_transpose_y_0 = const()[name = tensor("attn_263_transpose_y_0"), val = tensor(true)]; + tensor attn_263_cast_fp16 = matmul(transpose_x = attn_263_transpose_x_0, transpose_y = attn_263_transpose_y_0, x = var_12901_cast_fp16, y = var_12905_cast_fp16)[name = tensor("attn_263_cast_fp16")]; + tensor var_12909 = const()[name = tensor("op_12909"), val = tensor([1, 640, 1, -1])]; + tensor input_747_cast_fp16 = reshape(shape = var_12909, x = attn_263_cast_fp16)[name = tensor("input_747_cast_fp16")]; + tensor var_12914 = const()[name = tensor("op_12914"), val = tensor([1, 1])]; + tensor var_12916 = const()[name = tensor("op_12916"), val = tensor([1, 1])]; + tensor var_12918_pad_type_0 = const()[name = tensor("op_12918_pad_type_0"), val = tensor("custom")]; + tensor var_12918_pad_0 = const()[name = tensor("op_12918_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1862706816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863014080))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863014272)))]; + tensor var_12918_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_12916, groups = var_12518, pad = var_12918_pad_0, pad_type = var_12918_pad_type_0, strides = var_12914, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_747_cast_fp16)[name = tensor("op_12918_cast_fp16")]; + tensor inputs_395_cast_fp16 = add(x = var_12918_cast_fp16, y = inputs_393_cast_fp16)[name = tensor("inputs_395_cast_fp16")]; + tensor input_749_axes_0 = const()[name = tensor("input_749_axes_0"), val = tensor([1])]; + tensor input_749_gamma_0_to_fp16 = const()[name = tensor("input_749_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863015616)))]; + tensor input_749_beta_0_to_fp16 = const()[name = tensor("input_749_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863016960)))]; + tensor var_12928_to_fp16 = const()[name = tensor("op_12928_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = input_749_beta_0_to_fp16, epsilon = var_12928_to_fp16, gamma = input_749_gamma_0_to_fp16, x = inputs_395_cast_fp16)[name = tensor("input_749_cast_fp16")]; + tensor var_12944 = const()[name = tensor("op_12944"), val = tensor([1, 1])]; + tensor var_12946 = const()[name = tensor("op_12946"), val = tensor([1, 1])]; + tensor var_12948_pad_type_0 = const()[name = tensor("op_12948_pad_type_0"), val = tensor("custom")]; + tensor var_12948_pad_0 = const()[name = tensor("op_12948_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1863018304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865475968))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865476160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865480064))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_12948_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_12946, groups = var_12518, pad = var_12948_pad_0, pad_type = var_12948_pad_type_0, strides = var_12944, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_749_cast_fp16)[name = tensor("op_12948_cast_fp16")]; + tensor var_12949_split_sizes_0 = const()[name = tensor("op_12949_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12949_axis_0 = const()[name = tensor("op_12949_axis_0"), val = tensor(1)]; + tensor var_12949_cast_fp16_0, tensor var_12949_cast_fp16_1 = split(axis = var_12949_axis_0, split_sizes = var_12949_split_sizes_0, x = var_12948_cast_fp16)[name = tensor("op_12949_cast_fp16")]; + tensor var_12951_mode_0 = const()[name = tensor("op_12951_mode_0"), val = tensor("EXACT")]; + tensor var_12951_cast_fp16 = gelu(mode = var_12951_mode_0, x = var_12949_cast_fp16_1)[name = tensor("op_12951_cast_fp16")]; + tensor input_751_cast_fp16 = mul(x = var_12949_cast_fp16_0, y = var_12951_cast_fp16)[name = tensor("input_751_cast_fp16")]; + tensor var_12955 = const()[name = tensor("op_12955"), val = tensor([1, 1])]; + tensor var_12957 = const()[name = tensor("op_12957"), val = tensor([1, 1])]; + tensor var_12959_pad_type_0 = const()[name = tensor("op_12959_pad_type_0"), val = tensor("custom")]; + tensor var_12959_pad_0 = const()[name = tensor("op_12959_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865480256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866709120))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866709312)))]; + tensor var_12959_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_12957, groups = var_12518, pad = var_12959_pad_0, pad_type = var_12959_pad_type_0, strides = var_12955, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_751_cast_fp16)[name = tensor("op_12959_cast_fp16")]; + tensor hidden_states_521_cast_fp16 = add(x = var_12959_cast_fp16, y = inputs_395_cast_fp16)[name = tensor("hidden_states_521_cast_fp16")]; + tensor var_12961 = const()[name = tensor("op_12961"), val = tensor([1, 640, 64, 64])]; + tensor input_753_cast_fp16 = reshape(shape = var_12961, x = hidden_states_521_cast_fp16)[name = tensor("input_753_cast_fp16")]; + tensor var_12965 = const()[name = tensor("op_12965"), val = tensor([1, 1])]; + tensor var_12967 = const()[name = tensor("op_12967"), val = tensor([1, 1])]; + tensor hidden_states_523_pad_type_0 = const()[name = tensor("hidden_states_523_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_523_pad_0 = const()[name = tensor("hidden_states_523_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866710656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867017920))), name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867018112)))]; + tensor hidden_states_523_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_12967, groups = var_12518, pad = hidden_states_523_pad_0, pad_type = hidden_states_523_pad_type_0, strides = var_12965, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_753_cast_fp16)[name = tensor("hidden_states_523_cast_fp16")]; + tensor hidden_states_525_cast_fp16 = add(x = hidden_states_523_cast_fp16, y = hidden_states_505_cast_fp16)[name = tensor("hidden_states_525_cast_fp16")]; + tensor input_755_interleave_0 = const()[name = tensor("input_755_interleave_0"), val = tensor(false)]; + tensor input_755_cast_fp16 = concat(axis = var_12518, interleave = input_755_interleave_0, values = (hidden_states_525_cast_fp16, input_79_cast_fp16))[name = tensor("input_755_cast_fp16")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([1, 32, 40, 64, 64])]; + tensor reshape_132_cast_fp16 = reshape(shape = reshape_132_shape_0, x = input_755_cast_fp16)[name = tensor("reshape_132_cast_fp16")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast_fp16)[name = tensor("reduce_mean_99_cast_fp16")]; + tensor sub_66_cast_fp16 = sub(x = reshape_132_cast_fp16, y = reduce_mean_99_cast_fp16)[name = tensor("sub_66_cast_fp16")]; + tensor square_33_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor("square_33_cast_fp16")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast_fp16)[name = tensor("reduce_mean_101_cast_fp16")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast_fp16 = add(x = reduce_mean_101_cast_fp16, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast_fp16")]; + tensor sqrt_33_cast_fp16 = sqrt(x = add_66_cast_fp16)[name = tensor("sqrt_33_cast_fp16")]; + tensor real_div_33_cast_fp16 = real_div(x = sub_66_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor("real_div_33_cast_fp16")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([1, 1280, 64, 64])]; + tensor reshape_133_cast_fp16 = reshape(shape = reshape_133_shape_0, x = real_div_33_cast_fp16)[name = tensor("reshape_133_cast_fp16")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867019456)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867022080)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast_fp16 = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_133_cast_fp16)[name = tensor("add_67_cast_fp16")]; + tensor input_759_cast_fp16 = silu(x = add_67_cast_fp16)[name = tensor("input_759_cast_fp16")]; + tensor var_12985 = const()[name = tensor("op_12985"), val = tensor([1, 1])]; + tensor var_12987 = const()[name = tensor("op_12987"), val = tensor([1, 1])]; + tensor hidden_states_527_pad_type_0 = const()[name = tensor("hidden_states_527_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_527_pad_0 = const()[name = tensor("hidden_states_527_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1867024704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1872554368))), name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1280, 3, 3])]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1872554560)))]; + tensor hidden_states_527_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_12987, groups = var_12518, pad = hidden_states_527_pad_0, pad_type = hidden_states_527_pad_type_0, strides = var_12985, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_759_cast_fp16)[name = tensor("hidden_states_527_cast_fp16")]; + tensor var_12993 = const()[name = tensor("op_12993"), val = tensor([1, 1])]; + tensor var_12995 = const()[name = tensor("op_12995"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1872555904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873170368))), name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873170560)))]; + tensor temb_25_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_12995, groups = var_12518, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_12993, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_25_cast_fp16")]; + tensor input_763_cast_fp16 = add(x = hidden_states_527_cast_fp16, y = temb_25_cast_fp16)[name = tensor("input_763_cast_fp16")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_136_cast_fp16 = reshape(shape = reshape_136_shape_0, x = input_763_cast_fp16)[name = tensor("reshape_136_cast_fp16")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast_fp16 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast_fp16)[name = tensor("reduce_mean_102_cast_fp16")]; + tensor sub_68_cast_fp16 = sub(x = reshape_136_cast_fp16, y = reduce_mean_102_cast_fp16)[name = tensor("sub_68_cast_fp16")]; + tensor square_34_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor("square_34_cast_fp16")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast_fp16 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast_fp16)[name = tensor("reduce_mean_104_cast_fp16")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast_fp16 = add(x = reduce_mean_104_cast_fp16, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast_fp16")]; + tensor sqrt_34_cast_fp16 = sqrt(x = add_68_cast_fp16)[name = tensor("sqrt_34_cast_fp16")]; + tensor real_div_34_cast_fp16 = real_div(x = sub_68_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor("real_div_34_cast_fp16")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_137_cast_fp16 = reshape(shape = reshape_137_shape_0, x = real_div_34_cast_fp16)[name = tensor("reshape_137_cast_fp16")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873171904)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873173248)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast_fp16 = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_137_cast_fp16)[name = tensor("add_69_cast_fp16")]; + tensor input_767_cast_fp16 = silu(x = add_69_cast_fp16)[name = tensor("input_767_cast_fp16")]; + tensor var_13005 = const()[name = tensor("op_13005"), val = tensor([1, 1])]; + tensor var_13007 = const()[name = tensor("op_13007"), val = tensor([1, 1])]; + tensor hidden_states_529_pad_type_0 = const()[name = tensor("hidden_states_529_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_529_pad_0 = const()[name = tensor("hidden_states_529_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873174592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1875939456))), name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1875939648)))]; + tensor hidden_states_529_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_13007, groups = var_12518, pad = hidden_states_529_pad_0, pad_type = hidden_states_529_pad_type_0, strides = var_13005, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_767_cast_fp16)[name = tensor("hidden_states_529_cast_fp16")]; + tensor var_13012 = const()[name = tensor("op_13012"), val = tensor([1, 1])]; + tensor var_13014 = const()[name = tensor("op_13014"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1875940992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876555456))), name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876555648)))]; + tensor x_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13014, groups = var_12518, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_13012, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_755_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor hidden_states_531_cast_fp16 = add(x = x_13_cast_fp16, y = hidden_states_529_cast_fp16)[name = tensor("hidden_states_531_cast_fp16")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_140_cast_fp16 = reshape(shape = reshape_140_shape_0, x = hidden_states_531_cast_fp16)[name = tensor("reshape_140_cast_fp16")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast_fp16)[name = tensor("reduce_mean_105_cast_fp16")]; + tensor sub_70_cast_fp16 = sub(x = reshape_140_cast_fp16, y = reduce_mean_105_cast_fp16)[name = tensor("sub_70_cast_fp16")]; + tensor square_35_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor("square_35_cast_fp16")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast_fp16)[name = tensor("reduce_mean_107_cast_fp16")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast_fp16 = add(x = reduce_mean_107_cast_fp16, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast_fp16")]; + tensor sqrt_35_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor("sqrt_35_cast_fp16")]; + tensor real_div_35_cast_fp16 = real_div(x = sub_70_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor("real_div_35_cast_fp16")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_141_cast_fp16 = reshape(shape = reshape_141_shape_0, x = real_div_35_cast_fp16)[name = tensor("reshape_141_cast_fp16")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876556992)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876558336)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast_fp16 = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_141_cast_fp16)[name = tensor("add_71_cast_fp16")]; + tensor var_13036 = const()[name = tensor("op_13036"), val = tensor([1, 1])]; + tensor var_13038 = const()[name = tensor("op_13038"), val = tensor([1, 1])]; + tensor hidden_states_533_pad_type_0 = const()[name = tensor("hidden_states_533_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_533_pad_0 = const()[name = tensor("hidden_states_533_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876559680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876866944))), name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876867136)))]; + tensor hidden_states_533_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_13038, groups = var_12518, pad = hidden_states_533_pad_0, pad_type = hidden_states_533_pad_type_0, strides = var_13036, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_71_cast_fp16)[name = tensor("hidden_states_533_cast_fp16")]; + tensor var_13043 = const()[name = tensor("op_13043"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_397_cast_fp16 = reshape(shape = var_13043, x = hidden_states_533_cast_fp16)[name = tensor("inputs_397_cast_fp16")]; + tensor hidden_states_535_axes_0 = const()[name = tensor("hidden_states_535_axes_0"), val = tensor([1])]; + tensor hidden_states_535_gamma_0_to_fp16 = const()[name = tensor("hidden_states_535_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876868480)))]; + tensor hidden_states_535_beta_0_to_fp16 = const()[name = tensor("hidden_states_535_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876869824)))]; + tensor var_13059_to_fp16 = const()[name = tensor("op_13059_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_535_cast_fp16 = layer_norm(axes = hidden_states_535_axes_0, beta = hidden_states_535_beta_0_to_fp16, epsilon = var_13059_to_fp16, gamma = hidden_states_535_gamma_0_to_fp16, x = inputs_397_cast_fp16)[name = tensor("hidden_states_535_cast_fp16")]; + tensor var_13074 = const()[name = tensor("op_13074"), val = tensor([1, 1])]; + tensor var_13076 = const()[name = tensor("op_13076"), val = tensor([1, 1])]; + tensor q_265_pad_type_0 = const()[name = tensor("q_265_pad_type_0"), val = tensor("custom")]; + tensor q_265_pad_0 = const()[name = tensor("q_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876871168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877178432))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_265_cast_fp16 = conv(dilations = var_13076, groups = var_12518, pad = q_265_pad_0, pad_type = q_265_pad_type_0, strides = var_13074, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_535_cast_fp16)[name = tensor("q_265_cast_fp16")]; + tensor var_13080 = const()[name = tensor("op_13080"), val = tensor([1, 1])]; + tensor var_13082 = const()[name = tensor("op_13082"), val = tensor([1, 1])]; + tensor k_265_pad_type_0 = const()[name = tensor("k_265_pad_type_0"), val = tensor("custom")]; + tensor k_265_pad_0 = const()[name = tensor("k_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877178624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877485888))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_265_cast_fp16 = conv(dilations = var_13082, groups = var_12518, pad = k_265_pad_0, pad_type = k_265_pad_type_0, strides = var_13080, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_535_cast_fp16)[name = tensor("k_265_cast_fp16")]; + tensor var_13086 = const()[name = tensor("op_13086"), val = tensor([1, 1])]; + tensor var_13088 = const()[name = tensor("op_13088"), val = tensor([1, 1])]; + tensor v_265_pad_type_0 = const()[name = tensor("v_265_pad_type_0"), val = tensor("custom")]; + tensor v_265_pad_0 = const()[name = tensor("v_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877486080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877793344))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_265_cast_fp16 = conv(dilations = var_13088, groups = var_12518, pad = v_265_pad_0, pad_type = v_265_pad_type_0, strides = var_13086, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_535_cast_fp16)[name = tensor("v_265_cast_fp16")]; + tensor var_13092 = const()[name = tensor("op_13092"), val = tensor([1, 10, 64, -1])]; + tensor var_13093_cast_fp16 = reshape(shape = var_13092, x = q_265_cast_fp16)[name = tensor("op_13093_cast_fp16")]; + tensor var_13094 = const()[name = tensor("op_13094"), val = tensor([1, 10, 64, -1])]; + tensor var_13095_cast_fp16 = reshape(shape = var_13094, x = k_265_cast_fp16)[name = tensor("op_13095_cast_fp16")]; + tensor var_13096 = const()[name = tensor("op_13096"), val = tensor([1, 10, 64, -1])]; + tensor var_13097_cast_fp16 = reshape(shape = var_13096, x = v_265_cast_fp16)[name = tensor("op_13097_cast_fp16")]; + tensor attn_weights_529_transpose_x_0 = const()[name = tensor("attn_weights_529_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_529_transpose_y_0 = const()[name = tensor("attn_weights_529_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_529_cast_fp16 = matmul(transpose_x = attn_weights_529_transpose_x_0, transpose_y = attn_weights_529_transpose_y_0, x = var_13093_cast_fp16, y = var_13095_cast_fp16)[name = tensor("attn_weights_529_cast_fp16")]; + tensor attn_weights_531_cast_fp16 = mul(x = attn_weights_529_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_531_cast_fp16")]; + tensor var_13101_cast_fp16 = softmax(axis = var_12502, x = attn_weights_531_cast_fp16)[name = tensor("op_13101_cast_fp16")]; + tensor attn_265_transpose_x_0 = const()[name = tensor("attn_265_transpose_x_0"), val = tensor(false)]; + tensor attn_265_transpose_y_0 = const()[name = tensor("attn_265_transpose_y_0"), val = tensor(true)]; + tensor attn_265_cast_fp16 = matmul(transpose_x = attn_265_transpose_x_0, transpose_y = attn_265_transpose_y_0, x = var_13097_cast_fp16, y = var_13101_cast_fp16)[name = tensor("attn_265_cast_fp16")]; + tensor var_13105 = const()[name = tensor("op_13105"), val = tensor([1, 640, 1, -1])]; + tensor input_771_cast_fp16 = reshape(shape = var_13105, x = attn_265_cast_fp16)[name = tensor("input_771_cast_fp16")]; + tensor var_13110 = const()[name = tensor("op_13110"), val = tensor([1, 1])]; + tensor var_13112 = const()[name = tensor("op_13112"), val = tensor([1, 1])]; + tensor var_13114_pad_type_0 = const()[name = tensor("op_13114_pad_type_0"), val = tensor("custom")]; + tensor var_13114_pad_0 = const()[name = tensor("op_13114_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877793536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878100800))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878100992)))]; + tensor var_13114_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13112, groups = var_12518, pad = var_13114_pad_0, pad_type = var_13114_pad_type_0, strides = var_13110, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = tensor("op_13114_cast_fp16")]; + tensor inputs_399_cast_fp16 = add(x = var_13114_cast_fp16, y = inputs_397_cast_fp16)[name = tensor("inputs_399_cast_fp16")]; + tensor hidden_states_537_axes_0 = const()[name = tensor("hidden_states_537_axes_0"), val = tensor([1])]; + tensor hidden_states_537_gamma_0_to_fp16 = const()[name = tensor("hidden_states_537_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878102336)))]; + tensor hidden_states_537_beta_0_to_fp16 = const()[name = tensor("hidden_states_537_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878103680)))]; + tensor var_13124_to_fp16 = const()[name = tensor("op_13124_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_537_cast_fp16 = layer_norm(axes = hidden_states_537_axes_0, beta = hidden_states_537_beta_0_to_fp16, epsilon = var_13124_to_fp16, gamma = hidden_states_537_gamma_0_to_fp16, x = inputs_399_cast_fp16)[name = tensor("hidden_states_537_cast_fp16")]; + tensor var_13139 = const()[name = tensor("op_13139"), val = tensor([1, 1])]; + tensor var_13141 = const()[name = tensor("op_13141"), val = tensor([1, 1])]; + tensor q_267_pad_type_0 = const()[name = tensor("q_267_pad_type_0"), val = tensor("custom")]; + tensor q_267_pad_0 = const()[name = tensor("q_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878105024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878412288))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_267_cast_fp16 = conv(dilations = var_13141, groups = var_12518, pad = q_267_pad_0, pad_type = q_267_pad_type_0, strides = var_13139, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_537_cast_fp16)[name = tensor("q_267_cast_fp16")]; + tensor var_13145 = const()[name = tensor("op_13145"), val = tensor([1, 1])]; + tensor var_13147 = const()[name = tensor("op_13147"), val = tensor([1, 1])]; + tensor k_267_pad_type_0 = const()[name = tensor("k_267_pad_type_0"), val = tensor("custom")]; + tensor k_267_pad_0 = const()[name = tensor("k_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878412480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1879395584))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_267_cast_fp16 = conv(dilations = var_13147, groups = var_12518, pad = k_267_pad_0, pad_type = k_267_pad_type_0, strides = var_13145, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_267_cast_fp16")]; + tensor var_13151 = const()[name = tensor("op_13151"), val = tensor([1, 1])]; + tensor var_13153 = const()[name = tensor("op_13153"), val = tensor([1, 1])]; + tensor v_267_pad_type_0 = const()[name = tensor("v_267_pad_type_0"), val = tensor("custom")]; + tensor v_267_pad_0 = const()[name = tensor("v_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1879395776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880378880))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_267_cast_fp16 = conv(dilations = var_13153, groups = var_12518, pad = v_267_pad_0, pad_type = v_267_pad_type_0, strides = var_13151, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_267_cast_fp16")]; + tensor var_13157 = const()[name = tensor("op_13157"), val = tensor([1, 10, 64, -1])]; + tensor var_13158_cast_fp16 = reshape(shape = var_13157, x = q_267_cast_fp16)[name = tensor("op_13158_cast_fp16")]; + tensor var_13159 = const()[name = tensor("op_13159"), val = tensor([1, 10, 64, -1])]; + tensor var_13160_cast_fp16 = reshape(shape = var_13159, x = k_267_cast_fp16)[name = tensor("op_13160_cast_fp16")]; + tensor var_13161 = const()[name = tensor("op_13161"), val = tensor([1, 10, 64, -1])]; + tensor var_13162_cast_fp16 = reshape(shape = var_13161, x = v_267_cast_fp16)[name = tensor("op_13162_cast_fp16")]; + tensor attn_weights_533_transpose_x_0 = const()[name = tensor("attn_weights_533_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_533_transpose_y_0 = const()[name = tensor("attn_weights_533_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_533_cast_fp16 = matmul(transpose_x = attn_weights_533_transpose_x_0, transpose_y = attn_weights_533_transpose_y_0, x = var_13158_cast_fp16, y = var_13160_cast_fp16)[name = tensor("attn_weights_533_cast_fp16")]; + tensor attn_weights_535_cast_fp16 = mul(x = attn_weights_533_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_535_cast_fp16")]; + tensor var_13166_cast_fp16 = softmax(axis = var_12502, x = attn_weights_535_cast_fp16)[name = tensor("op_13166_cast_fp16")]; + tensor attn_267_transpose_x_0 = const()[name = tensor("attn_267_transpose_x_0"), val = tensor(false)]; + tensor attn_267_transpose_y_0 = const()[name = tensor("attn_267_transpose_y_0"), val = tensor(true)]; + tensor attn_267_cast_fp16 = matmul(transpose_x = attn_267_transpose_x_0, transpose_y = attn_267_transpose_y_0, x = var_13162_cast_fp16, y = var_13166_cast_fp16)[name = tensor("attn_267_cast_fp16")]; + tensor var_13170 = const()[name = tensor("op_13170"), val = tensor([1, 640, 1, -1])]; + tensor input_773_cast_fp16 = reshape(shape = var_13170, x = attn_267_cast_fp16)[name = tensor("input_773_cast_fp16")]; + tensor var_13175 = const()[name = tensor("op_13175"), val = tensor([1, 1])]; + tensor var_13177 = const()[name = tensor("op_13177"), val = tensor([1, 1])]; + tensor var_13179_pad_type_0 = const()[name = tensor("op_13179_pad_type_0"), val = tensor("custom")]; + tensor var_13179_pad_0 = const()[name = tensor("op_13179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880379072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880686336))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880686528)))]; + tensor var_13179_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13177, groups = var_12518, pad = var_13179_pad_0, pad_type = var_13179_pad_type_0, strides = var_13175, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_773_cast_fp16)[name = tensor("op_13179_cast_fp16")]; + tensor inputs_401_cast_fp16 = add(x = var_13179_cast_fp16, y = inputs_399_cast_fp16)[name = tensor("inputs_401_cast_fp16")]; + tensor input_775_axes_0 = const()[name = tensor("input_775_axes_0"), val = tensor([1])]; + tensor input_775_gamma_0_to_fp16 = const()[name = tensor("input_775_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880687872)))]; + tensor input_775_beta_0_to_fp16 = const()[name = tensor("input_775_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880689216)))]; + tensor var_13189_to_fp16 = const()[name = tensor("op_13189_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_775_cast_fp16 = layer_norm(axes = input_775_axes_0, beta = input_775_beta_0_to_fp16, epsilon = var_13189_to_fp16, gamma = input_775_gamma_0_to_fp16, x = inputs_401_cast_fp16)[name = tensor("input_775_cast_fp16")]; + tensor var_13205 = const()[name = tensor("op_13205"), val = tensor([1, 1])]; + tensor var_13207 = const()[name = tensor("op_13207"), val = tensor([1, 1])]; + tensor var_13209_pad_type_0 = const()[name = tensor("op_13209_pad_type_0"), val = tensor("custom")]; + tensor var_13209_pad_0 = const()[name = tensor("op_13209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880690560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1883148224))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1883148416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1883152320))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13209_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13207, groups = var_12518, pad = var_13209_pad_0, pad_type = var_13209_pad_type_0, strides = var_13205, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_775_cast_fp16)[name = tensor("op_13209_cast_fp16")]; + tensor var_13210_split_sizes_0 = const()[name = tensor("op_13210_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13210_axis_0 = const()[name = tensor("op_13210_axis_0"), val = tensor(1)]; + tensor var_13210_cast_fp16_0, tensor var_13210_cast_fp16_1 = split(axis = var_13210_axis_0, split_sizes = var_13210_split_sizes_0, x = var_13209_cast_fp16)[name = tensor("op_13210_cast_fp16")]; + tensor var_13212_mode_0 = const()[name = tensor("op_13212_mode_0"), val = tensor("EXACT")]; + tensor var_13212_cast_fp16 = gelu(mode = var_13212_mode_0, x = var_13210_cast_fp16_1)[name = tensor("op_13212_cast_fp16")]; + tensor input_777_cast_fp16 = mul(x = var_13210_cast_fp16_0, y = var_13212_cast_fp16)[name = tensor("input_777_cast_fp16")]; + tensor var_13216 = const()[name = tensor("op_13216"), val = tensor([1, 1])]; + tensor var_13218 = const()[name = tensor("op_13218"), val = tensor([1, 1])]; + tensor var_13220_pad_type_0 = const()[name = tensor("op_13220_pad_type_0"), val = tensor("custom")]; + tensor var_13220_pad_0 = const()[name = tensor("op_13220_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1883152512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884381376))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884381568)))]; + tensor var_13220_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13218, groups = var_12518, pad = var_13220_pad_0, pad_type = var_13220_pad_type_0, strides = var_13216, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_777_cast_fp16)[name = tensor("op_13220_cast_fp16")]; + tensor inputs_403_cast_fp16 = add(x = var_13220_cast_fp16, y = inputs_401_cast_fp16)[name = tensor("inputs_403_cast_fp16")]; + tensor hidden_states_541_axes_0 = const()[name = tensor("hidden_states_541_axes_0"), val = tensor([1])]; + tensor hidden_states_541_gamma_0_to_fp16 = const()[name = tensor("hidden_states_541_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884382912)))]; + tensor hidden_states_541_beta_0_to_fp16 = const()[name = tensor("hidden_states_541_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884384256)))]; + tensor var_13236_to_fp16 = const()[name = tensor("op_13236_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_541_cast_fp16 = layer_norm(axes = hidden_states_541_axes_0, beta = hidden_states_541_beta_0_to_fp16, epsilon = var_13236_to_fp16, gamma = hidden_states_541_gamma_0_to_fp16, x = inputs_403_cast_fp16)[name = tensor("hidden_states_541_cast_fp16")]; + tensor var_13251 = const()[name = tensor("op_13251"), val = tensor([1, 1])]; + tensor var_13253 = const()[name = tensor("op_13253"), val = tensor([1, 1])]; + tensor q_269_pad_type_0 = const()[name = tensor("q_269_pad_type_0"), val = tensor("custom")]; + tensor q_269_pad_0 = const()[name = tensor("q_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884385600))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884692864))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_269_cast_fp16 = conv(dilations = var_13253, groups = var_12518, pad = q_269_pad_0, pad_type = q_269_pad_type_0, strides = var_13251, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_541_cast_fp16)[name = tensor("q_269_cast_fp16")]; + tensor var_13257 = const()[name = tensor("op_13257"), val = tensor([1, 1])]; + tensor var_13259 = const()[name = tensor("op_13259"), val = tensor([1, 1])]; + tensor k_269_pad_type_0 = const()[name = tensor("k_269_pad_type_0"), val = tensor("custom")]; + tensor k_269_pad_0 = const()[name = tensor("k_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1884693056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885000320))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_269_cast_fp16 = conv(dilations = var_13259, groups = var_12518, pad = k_269_pad_0, pad_type = k_269_pad_type_0, strides = var_13257, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_541_cast_fp16)[name = tensor("k_269_cast_fp16")]; + tensor var_13263 = const()[name = tensor("op_13263"), val = tensor([1, 1])]; + tensor var_13265 = const()[name = tensor("op_13265"), val = tensor([1, 1])]; + tensor v_269_pad_type_0 = const()[name = tensor("v_269_pad_type_0"), val = tensor("custom")]; + tensor v_269_pad_0 = const()[name = tensor("v_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885000512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885307776))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_269_cast_fp16 = conv(dilations = var_13265, groups = var_12518, pad = v_269_pad_0, pad_type = v_269_pad_type_0, strides = var_13263, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_541_cast_fp16)[name = tensor("v_269_cast_fp16")]; + tensor var_13269 = const()[name = tensor("op_13269"), val = tensor([1, 10, 64, -1])]; + tensor var_13270_cast_fp16 = reshape(shape = var_13269, x = q_269_cast_fp16)[name = tensor("op_13270_cast_fp16")]; + tensor var_13271 = const()[name = tensor("op_13271"), val = tensor([1, 10, 64, -1])]; + tensor var_13272_cast_fp16 = reshape(shape = var_13271, x = k_269_cast_fp16)[name = tensor("op_13272_cast_fp16")]; + tensor var_13273 = const()[name = tensor("op_13273"), val = tensor([1, 10, 64, -1])]; + tensor var_13274_cast_fp16 = reshape(shape = var_13273, x = v_269_cast_fp16)[name = tensor("op_13274_cast_fp16")]; + tensor attn_weights_537_transpose_x_0 = const()[name = tensor("attn_weights_537_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_537_transpose_y_0 = const()[name = tensor("attn_weights_537_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_537_cast_fp16 = matmul(transpose_x = attn_weights_537_transpose_x_0, transpose_y = attn_weights_537_transpose_y_0, x = var_13270_cast_fp16, y = var_13272_cast_fp16)[name = tensor("attn_weights_537_cast_fp16")]; + tensor attn_weights_539_cast_fp16 = mul(x = attn_weights_537_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_539_cast_fp16")]; + tensor var_13278_cast_fp16 = softmax(axis = var_12502, x = attn_weights_539_cast_fp16)[name = tensor("op_13278_cast_fp16")]; + tensor attn_269_transpose_x_0 = const()[name = tensor("attn_269_transpose_x_0"), val = tensor(false)]; + tensor attn_269_transpose_y_0 = const()[name = tensor("attn_269_transpose_y_0"), val = tensor(true)]; + tensor attn_269_cast_fp16 = matmul(transpose_x = attn_269_transpose_x_0, transpose_y = attn_269_transpose_y_0, x = var_13274_cast_fp16, y = var_13278_cast_fp16)[name = tensor("attn_269_cast_fp16")]; + tensor var_13282 = const()[name = tensor("op_13282"), val = tensor([1, 640, 1, -1])]; + tensor input_779_cast_fp16 = reshape(shape = var_13282, x = attn_269_cast_fp16)[name = tensor("input_779_cast_fp16")]; + tensor var_13287 = const()[name = tensor("op_13287"), val = tensor([1, 1])]; + tensor var_13289 = const()[name = tensor("op_13289"), val = tensor([1, 1])]; + tensor var_13291_pad_type_0 = const()[name = tensor("op_13291_pad_type_0"), val = tensor("custom")]; + tensor var_13291_pad_0 = const()[name = tensor("op_13291_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885307968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885615232))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885615424)))]; + tensor var_13291_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13289, groups = var_12518, pad = var_13291_pad_0, pad_type = var_13291_pad_type_0, strides = var_13287, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_779_cast_fp16)[name = tensor("op_13291_cast_fp16")]; + tensor inputs_405_cast_fp16 = add(x = var_13291_cast_fp16, y = inputs_403_cast_fp16)[name = tensor("inputs_405_cast_fp16")]; + tensor hidden_states_543_axes_0 = const()[name = tensor("hidden_states_543_axes_0"), val = tensor([1])]; + tensor hidden_states_543_gamma_0_to_fp16 = const()[name = tensor("hidden_states_543_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885616768)))]; + tensor hidden_states_543_beta_0_to_fp16 = const()[name = tensor("hidden_states_543_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885618112)))]; + tensor var_13301_to_fp16 = const()[name = tensor("op_13301_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_543_cast_fp16 = layer_norm(axes = hidden_states_543_axes_0, beta = hidden_states_543_beta_0_to_fp16, epsilon = var_13301_to_fp16, gamma = hidden_states_543_gamma_0_to_fp16, x = inputs_405_cast_fp16)[name = tensor("hidden_states_543_cast_fp16")]; + tensor var_13316 = const()[name = tensor("op_13316"), val = tensor([1, 1])]; + tensor var_13318 = const()[name = tensor("op_13318"), val = tensor([1, 1])]; + tensor q_271_pad_type_0 = const()[name = tensor("q_271_pad_type_0"), val = tensor("custom")]; + tensor q_271_pad_0 = const()[name = tensor("q_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885619456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885926720))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_271_cast_fp16 = conv(dilations = var_13318, groups = var_12518, pad = q_271_pad_0, pad_type = q_271_pad_type_0, strides = var_13316, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_543_cast_fp16)[name = tensor("q_271_cast_fp16")]; + tensor var_13322 = const()[name = tensor("op_13322"), val = tensor([1, 1])]; + tensor var_13324 = const()[name = tensor("op_13324"), val = tensor([1, 1])]; + tensor k_271_pad_type_0 = const()[name = tensor("k_271_pad_type_0"), val = tensor("custom")]; + tensor k_271_pad_0 = const()[name = tensor("k_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1885926912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1886910016))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_271_cast_fp16 = conv(dilations = var_13324, groups = var_12518, pad = k_271_pad_0, pad_type = k_271_pad_type_0, strides = var_13322, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_271_cast_fp16")]; + tensor var_13328 = const()[name = tensor("op_13328"), val = tensor([1, 1])]; + tensor var_13330 = const()[name = tensor("op_13330"), val = tensor([1, 1])]; + tensor v_271_pad_type_0 = const()[name = tensor("v_271_pad_type_0"), val = tensor("custom")]; + tensor v_271_pad_0 = const()[name = tensor("v_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1886910208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1887893312))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_271_cast_fp16 = conv(dilations = var_13330, groups = var_12518, pad = v_271_pad_0, pad_type = v_271_pad_type_0, strides = var_13328, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_271_cast_fp16")]; + tensor var_13334 = const()[name = tensor("op_13334"), val = tensor([1, 10, 64, -1])]; + tensor var_13335_cast_fp16 = reshape(shape = var_13334, x = q_271_cast_fp16)[name = tensor("op_13335_cast_fp16")]; + tensor var_13336 = const()[name = tensor("op_13336"), val = tensor([1, 10, 64, -1])]; + tensor var_13337_cast_fp16 = reshape(shape = var_13336, x = k_271_cast_fp16)[name = tensor("op_13337_cast_fp16")]; + tensor var_13338 = const()[name = tensor("op_13338"), val = tensor([1, 10, 64, -1])]; + tensor var_13339_cast_fp16 = reshape(shape = var_13338, x = v_271_cast_fp16)[name = tensor("op_13339_cast_fp16")]; + tensor attn_weights_541_transpose_x_0 = const()[name = tensor("attn_weights_541_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_541_transpose_y_0 = const()[name = tensor("attn_weights_541_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_541_cast_fp16 = matmul(transpose_x = attn_weights_541_transpose_x_0, transpose_y = attn_weights_541_transpose_y_0, x = var_13335_cast_fp16, y = var_13337_cast_fp16)[name = tensor("attn_weights_541_cast_fp16")]; + tensor attn_weights_543_cast_fp16 = mul(x = attn_weights_541_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_543_cast_fp16")]; + tensor var_13343_cast_fp16 = softmax(axis = var_12502, x = attn_weights_543_cast_fp16)[name = tensor("op_13343_cast_fp16")]; + tensor attn_271_transpose_x_0 = const()[name = tensor("attn_271_transpose_x_0"), val = tensor(false)]; + tensor attn_271_transpose_y_0 = const()[name = tensor("attn_271_transpose_y_0"), val = tensor(true)]; + tensor attn_271_cast_fp16 = matmul(transpose_x = attn_271_transpose_x_0, transpose_y = attn_271_transpose_y_0, x = var_13339_cast_fp16, y = var_13343_cast_fp16)[name = tensor("attn_271_cast_fp16")]; + tensor var_13347 = const()[name = tensor("op_13347"), val = tensor([1, 640, 1, -1])]; + tensor input_781_cast_fp16 = reshape(shape = var_13347, x = attn_271_cast_fp16)[name = tensor("input_781_cast_fp16")]; + tensor var_13352 = const()[name = tensor("op_13352"), val = tensor([1, 1])]; + tensor var_13354 = const()[name = tensor("op_13354"), val = tensor([1, 1])]; + tensor var_13356_pad_type_0 = const()[name = tensor("op_13356_pad_type_0"), val = tensor("custom")]; + tensor var_13356_pad_0 = const()[name = tensor("op_13356_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1887893504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888200768))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888200960)))]; + tensor var_13356_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13354, groups = var_12518, pad = var_13356_pad_0, pad_type = var_13356_pad_type_0, strides = var_13352, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_781_cast_fp16)[name = tensor("op_13356_cast_fp16")]; + tensor inputs_407_cast_fp16 = add(x = var_13356_cast_fp16, y = inputs_405_cast_fp16)[name = tensor("inputs_407_cast_fp16")]; + tensor input_783_axes_0 = const()[name = tensor("input_783_axes_0"), val = tensor([1])]; + tensor input_783_gamma_0_to_fp16 = const()[name = tensor("input_783_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888202304)))]; + tensor input_783_beta_0_to_fp16 = const()[name = tensor("input_783_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888203648)))]; + tensor var_13366_to_fp16 = const()[name = tensor("op_13366_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_783_cast_fp16 = layer_norm(axes = input_783_axes_0, beta = input_783_beta_0_to_fp16, epsilon = var_13366_to_fp16, gamma = input_783_gamma_0_to_fp16, x = inputs_407_cast_fp16)[name = tensor("input_783_cast_fp16")]; + tensor var_13382 = const()[name = tensor("op_13382"), val = tensor([1, 1])]; + tensor var_13384 = const()[name = tensor("op_13384"), val = tensor([1, 1])]; + tensor var_13386_pad_type_0 = const()[name = tensor("op_13386_pad_type_0"), val = tensor("custom")]; + tensor var_13386_pad_0 = const()[name = tensor("op_13386_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1888204992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890662656))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890662848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890666752))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13386_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13384, groups = var_12518, pad = var_13386_pad_0, pad_type = var_13386_pad_type_0, strides = var_13382, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_783_cast_fp16)[name = tensor("op_13386_cast_fp16")]; + tensor var_13387_split_sizes_0 = const()[name = tensor("op_13387_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13387_axis_0 = const()[name = tensor("op_13387_axis_0"), val = tensor(1)]; + tensor var_13387_cast_fp16_0, tensor var_13387_cast_fp16_1 = split(axis = var_13387_axis_0, split_sizes = var_13387_split_sizes_0, x = var_13386_cast_fp16)[name = tensor("op_13387_cast_fp16")]; + tensor var_13389_mode_0 = const()[name = tensor("op_13389_mode_0"), val = tensor("EXACT")]; + tensor var_13389_cast_fp16 = gelu(mode = var_13389_mode_0, x = var_13387_cast_fp16_1)[name = tensor("op_13389_cast_fp16")]; + tensor input_785_cast_fp16 = mul(x = var_13387_cast_fp16_0, y = var_13389_cast_fp16)[name = tensor("input_785_cast_fp16")]; + tensor var_13393 = const()[name = tensor("op_13393"), val = tensor([1, 1])]; + tensor var_13395 = const()[name = tensor("op_13395"), val = tensor([1, 1])]; + tensor var_13397_pad_type_0 = const()[name = tensor("op_13397_pad_type_0"), val = tensor("custom")]; + tensor var_13397_pad_0 = const()[name = tensor("op_13397_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1890666944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1891895808))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1891896000)))]; + tensor var_13397_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13395, groups = var_12518, pad = var_13397_pad_0, pad_type = var_13397_pad_type_0, strides = var_13393, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_785_cast_fp16)[name = tensor("op_13397_cast_fp16")]; + tensor hidden_states_547_cast_fp16 = add(x = var_13397_cast_fp16, y = inputs_407_cast_fp16)[name = tensor("hidden_states_547_cast_fp16")]; + tensor var_13399 = const()[name = tensor("op_13399"), val = tensor([1, 640, 64, 64])]; + tensor input_787_cast_fp16 = reshape(shape = var_13399, x = hidden_states_547_cast_fp16)[name = tensor("input_787_cast_fp16")]; + tensor var_13403 = const()[name = tensor("op_13403"), val = tensor([1, 1])]; + tensor var_13405 = const()[name = tensor("op_13405"), val = tensor([1, 1])]; + tensor hidden_states_549_pad_type_0 = const()[name = tensor("hidden_states_549_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_549_pad_0 = const()[name = tensor("hidden_states_549_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1891897344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892204608))), name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892204800)))]; + tensor hidden_states_549_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_13405, groups = var_12518, pad = hidden_states_549_pad_0, pad_type = hidden_states_549_pad_type_0, strides = var_13403, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_787_cast_fp16)[name = tensor("hidden_states_549_cast_fp16")]; + tensor hidden_states_551_cast_fp16 = add(x = hidden_states_549_cast_fp16, y = hidden_states_531_cast_fp16)[name = tensor("hidden_states_551_cast_fp16")]; + tensor input_789_interleave_0 = const()[name = tensor("input_789_interleave_0"), val = tensor(false)]; + tensor input_789_cast_fp16 = concat(axis = var_12518, interleave = input_789_interleave_0, values = (hidden_states_551_cast_fp16, input_45_cast_fp16))[name = tensor("input_789_cast_fp16")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([1, 32, 30, 64, 64])]; + tensor reshape_144_cast_fp16 = reshape(shape = reshape_144_shape_0, x = input_789_cast_fp16)[name = tensor("reshape_144_cast_fp16")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast_fp16)[name = tensor("reduce_mean_108_cast_fp16")]; + tensor sub_72_cast_fp16 = sub(x = reshape_144_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor("sub_72_cast_fp16")]; + tensor square_36_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor("square_36_cast_fp16")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast_fp16)[name = tensor("reduce_mean_110_cast_fp16")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast_fp16 = add(x = reduce_mean_110_cast_fp16, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast_fp16")]; + tensor sqrt_36_cast_fp16 = sqrt(x = add_72_cast_fp16)[name = tensor("sqrt_36_cast_fp16")]; + tensor real_div_36_cast_fp16 = real_div(x = sub_72_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor("real_div_36_cast_fp16")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([1, 960, 64, 64])]; + tensor reshape_145_cast_fp16 = reshape(shape = reshape_145_shape_0, x = real_div_36_cast_fp16)[name = tensor("reshape_145_cast_fp16")]; + tensor add_73_mean_0_to_fp16 = const()[name = tensor("add_73_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892206144)))]; + tensor add_73_variance_0_to_fp16 = const()[name = tensor("add_73_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892208128)))]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892210112)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892212096)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast_fp16 = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_145_cast_fp16)[name = tensor("add_73_cast_fp16")]; + tensor input_793_cast_fp16 = silu(x = add_73_cast_fp16)[name = tensor("input_793_cast_fp16")]; + tensor var_13423 = const()[name = tensor("op_13423"), val = tensor([1, 1])]; + tensor var_13425 = const()[name = tensor("op_13425"), val = tensor([1, 1])]; + tensor hidden_states_553_pad_type_0 = const()[name = tensor("hidden_states_553_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_553_pad_0 = const()[name = tensor("hidden_states_553_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892214080))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896361344))), name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([640, 960, 3, 3])]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896361536)))]; + tensor hidden_states_553_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_13425, groups = var_12518, pad = hidden_states_553_pad_0, pad_type = hidden_states_553_pad_type_0, strides = var_13423, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_793_cast_fp16)[name = tensor("hidden_states_553_cast_fp16")]; + tensor var_13431 = const()[name = tensor("op_13431"), val = tensor([1, 1])]; + tensor var_13433 = const()[name = tensor("op_13433"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896362880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896977344))), name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896977536)))]; + tensor temb_27_cast_fp16 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13433, groups = var_12518, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_13431, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_27_cast_fp16")]; + tensor input_797_cast_fp16 = add(x = hidden_states_553_cast_fp16, y = temb_27_cast_fp16)[name = tensor("input_797_cast_fp16")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_148_cast_fp16 = reshape(shape = reshape_148_shape_0, x = input_797_cast_fp16)[name = tensor("reshape_148_cast_fp16")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast_fp16)[name = tensor("reduce_mean_111_cast_fp16")]; + tensor sub_74_cast_fp16 = sub(x = reshape_148_cast_fp16, y = reduce_mean_111_cast_fp16)[name = tensor("sub_74_cast_fp16")]; + tensor square_37_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor("square_37_cast_fp16")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast_fp16)[name = tensor("reduce_mean_113_cast_fp16")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast_fp16 = add(x = reduce_mean_113_cast_fp16, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast_fp16")]; + tensor sqrt_37_cast_fp16 = sqrt(x = add_74_cast_fp16)[name = tensor("sqrt_37_cast_fp16")]; + tensor real_div_37_cast_fp16 = real_div(x = sub_74_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor("real_div_37_cast_fp16")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_149_cast_fp16 = reshape(shape = reshape_149_shape_0, x = real_div_37_cast_fp16)[name = tensor("reshape_149_cast_fp16")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896978880)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896980224)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast_fp16 = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_149_cast_fp16)[name = tensor("add_75_cast_fp16")]; + tensor input_801_cast_fp16 = silu(x = add_75_cast_fp16)[name = tensor("input_801_cast_fp16")]; + tensor var_13443 = const()[name = tensor("op_13443"), val = tensor([1, 1])]; + tensor var_13445 = const()[name = tensor("op_13445"), val = tensor([1, 1])]; + tensor hidden_states_555_pad_type_0 = const()[name = tensor("hidden_states_555_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_555_pad_0 = const()[name = tensor("hidden_states_555_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1896981568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1899746432))), name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1899746624)))]; + tensor hidden_states_555_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_13445, groups = var_12518, pad = hidden_states_555_pad_0, pad_type = hidden_states_555_pad_type_0, strides = var_13443, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_801_cast_fp16)[name = tensor("hidden_states_555_cast_fp16")]; + tensor var_13450 = const()[name = tensor("op_13450"), val = tensor([1, 1])]; + tensor var_13452 = const()[name = tensor("op_13452"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1899747968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900208832))), name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 960, 1, 1])]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900209024)))]; + tensor x_15_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_13452, groups = var_12518, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_13450, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_789_cast_fp16)[name = tensor("x_15_cast_fp16")]; + tensor hidden_states_557_cast_fp16 = add(x = x_15_cast_fp16, y = hidden_states_555_cast_fp16)[name = tensor("hidden_states_557_cast_fp16")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([1, 32, 20, 64, 64])]; + tensor reshape_152_cast_fp16 = reshape(shape = reshape_152_shape_0, x = hidden_states_557_cast_fp16)[name = tensor("reshape_152_cast_fp16")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast_fp16 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast_fp16)[name = tensor("reduce_mean_114_cast_fp16")]; + tensor sub_76_cast_fp16 = sub(x = reshape_152_cast_fp16, y = reduce_mean_114_cast_fp16)[name = tensor("sub_76_cast_fp16")]; + tensor square_38_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor("square_38_cast_fp16")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast_fp16 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast_fp16)[name = tensor("reduce_mean_116_cast_fp16")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast_fp16 = add(x = reduce_mean_116_cast_fp16, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast_fp16")]; + tensor sqrt_38_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor("sqrt_38_cast_fp16")]; + tensor real_div_38_cast_fp16 = real_div(x = sub_76_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor("real_div_38_cast_fp16")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([1, 640, 64, 64])]; + tensor reshape_153_cast_fp16 = reshape(shape = reshape_153_shape_0, x = real_div_38_cast_fp16)[name = tensor("reshape_153_cast_fp16")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900210368)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900211712)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast_fp16 = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_153_cast_fp16)[name = tensor("add_77_cast_fp16")]; + tensor var_13474 = const()[name = tensor("op_13474"), val = tensor([1, 1])]; + tensor var_13476 = const()[name = tensor("op_13476"), val = tensor([1, 1])]; + tensor hidden_states_559_pad_type_0 = const()[name = tensor("hidden_states_559_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_559_pad_0 = const()[name = tensor("hidden_states_559_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900213056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900520320))), name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900520512)))]; + tensor hidden_states_559_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_13476, groups = var_12518, pad = hidden_states_559_pad_0, pad_type = hidden_states_559_pad_type_0, strides = var_13474, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_77_cast_fp16)[name = tensor("hidden_states_559_cast_fp16")]; + tensor var_13481 = const()[name = tensor("op_13481"), val = tensor([1, 640, 1, 4096])]; + tensor inputs_409_cast_fp16 = reshape(shape = var_13481, x = hidden_states_559_cast_fp16)[name = tensor("inputs_409_cast_fp16")]; + tensor hidden_states_561_axes_0 = const()[name = tensor("hidden_states_561_axes_0"), val = tensor([1])]; + tensor hidden_states_561_gamma_0_to_fp16 = const()[name = tensor("hidden_states_561_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900521856)))]; + tensor hidden_states_561_beta_0_to_fp16 = const()[name = tensor("hidden_states_561_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900523200)))]; + tensor var_13497_to_fp16 = const()[name = tensor("op_13497_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_561_cast_fp16 = layer_norm(axes = hidden_states_561_axes_0, beta = hidden_states_561_beta_0_to_fp16, epsilon = var_13497_to_fp16, gamma = hidden_states_561_gamma_0_to_fp16, x = inputs_409_cast_fp16)[name = tensor("hidden_states_561_cast_fp16")]; + tensor var_13512 = const()[name = tensor("op_13512"), val = tensor([1, 1])]; + tensor var_13514 = const()[name = tensor("op_13514"), val = tensor([1, 1])]; + tensor q_273_pad_type_0 = const()[name = tensor("q_273_pad_type_0"), val = tensor("custom")]; + tensor q_273_pad_0 = const()[name = tensor("q_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900524544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900831808))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_273_cast_fp16 = conv(dilations = var_13514, groups = var_12518, pad = q_273_pad_0, pad_type = q_273_pad_type_0, strides = var_13512, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_561_cast_fp16)[name = tensor("q_273_cast_fp16")]; + tensor var_13518 = const()[name = tensor("op_13518"), val = tensor([1, 1])]; + tensor var_13520 = const()[name = tensor("op_13520"), val = tensor([1, 1])]; + tensor k_273_pad_type_0 = const()[name = tensor("k_273_pad_type_0"), val = tensor("custom")]; + tensor k_273_pad_0 = const()[name = tensor("k_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1900832000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901139264))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_273_cast_fp16 = conv(dilations = var_13520, groups = var_12518, pad = k_273_pad_0, pad_type = k_273_pad_type_0, strides = var_13518, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_561_cast_fp16)[name = tensor("k_273_cast_fp16")]; + tensor var_13524 = const()[name = tensor("op_13524"), val = tensor([1, 1])]; + tensor var_13526 = const()[name = tensor("op_13526"), val = tensor([1, 1])]; + tensor v_273_pad_type_0 = const()[name = tensor("v_273_pad_type_0"), val = tensor("custom")]; + tensor v_273_pad_0 = const()[name = tensor("v_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901139456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901446720))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_273_cast_fp16 = conv(dilations = var_13526, groups = var_12518, pad = v_273_pad_0, pad_type = v_273_pad_type_0, strides = var_13524, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_561_cast_fp16)[name = tensor("v_273_cast_fp16")]; + tensor var_13530 = const()[name = tensor("op_13530"), val = tensor([1, 10, 64, -1])]; + tensor var_13531_cast_fp16 = reshape(shape = var_13530, x = q_273_cast_fp16)[name = tensor("op_13531_cast_fp16")]; + tensor var_13532 = const()[name = tensor("op_13532"), val = tensor([1, 10, 64, -1])]; + tensor var_13533_cast_fp16 = reshape(shape = var_13532, x = k_273_cast_fp16)[name = tensor("op_13533_cast_fp16")]; + tensor var_13534 = const()[name = tensor("op_13534"), val = tensor([1, 10, 64, -1])]; + tensor var_13535_cast_fp16 = reshape(shape = var_13534, x = v_273_cast_fp16)[name = tensor("op_13535_cast_fp16")]; + tensor attn_weights_545_transpose_x_0 = const()[name = tensor("attn_weights_545_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_545_transpose_y_0 = const()[name = tensor("attn_weights_545_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_545_cast_fp16 = matmul(transpose_x = attn_weights_545_transpose_x_0, transpose_y = attn_weights_545_transpose_y_0, x = var_13531_cast_fp16, y = var_13533_cast_fp16)[name = tensor("attn_weights_545_cast_fp16")]; + tensor attn_weights_547_cast_fp16 = mul(x = attn_weights_545_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_547_cast_fp16")]; + tensor var_13539_cast_fp16 = softmax(axis = var_12502, x = attn_weights_547_cast_fp16)[name = tensor("op_13539_cast_fp16")]; + tensor attn_273_transpose_x_0 = const()[name = tensor("attn_273_transpose_x_0"), val = tensor(false)]; + tensor attn_273_transpose_y_0 = const()[name = tensor("attn_273_transpose_y_0"), val = tensor(true)]; + tensor attn_273_cast_fp16 = matmul(transpose_x = attn_273_transpose_x_0, transpose_y = attn_273_transpose_y_0, x = var_13535_cast_fp16, y = var_13539_cast_fp16)[name = tensor("attn_273_cast_fp16")]; + tensor var_13543 = const()[name = tensor("op_13543"), val = tensor([1, 640, 1, -1])]; + tensor input_805_cast_fp16 = reshape(shape = var_13543, x = attn_273_cast_fp16)[name = tensor("input_805_cast_fp16")]; + tensor var_13548 = const()[name = tensor("op_13548"), val = tensor([1, 1])]; + tensor var_13550 = const()[name = tensor("op_13550"), val = tensor([1, 1])]; + tensor var_13552_pad_type_0 = const()[name = tensor("op_13552_pad_type_0"), val = tensor("custom")]; + tensor var_13552_pad_0 = const()[name = tensor("op_13552_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901446912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901754176))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901754368)))]; + tensor var_13552_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13550, groups = var_12518, pad = var_13552_pad_0, pad_type = var_13552_pad_type_0, strides = var_13548, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_805_cast_fp16)[name = tensor("op_13552_cast_fp16")]; + tensor inputs_411_cast_fp16 = add(x = var_13552_cast_fp16, y = inputs_409_cast_fp16)[name = tensor("inputs_411_cast_fp16")]; + tensor hidden_states_563_axes_0 = const()[name = tensor("hidden_states_563_axes_0"), val = tensor([1])]; + tensor hidden_states_563_gamma_0_to_fp16 = const()[name = tensor("hidden_states_563_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901755712)))]; + tensor hidden_states_563_beta_0_to_fp16 = const()[name = tensor("hidden_states_563_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901757056)))]; + tensor var_13562_to_fp16 = const()[name = tensor("op_13562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_563_cast_fp16 = layer_norm(axes = hidden_states_563_axes_0, beta = hidden_states_563_beta_0_to_fp16, epsilon = var_13562_to_fp16, gamma = hidden_states_563_gamma_0_to_fp16, x = inputs_411_cast_fp16)[name = tensor("hidden_states_563_cast_fp16")]; + tensor var_13577 = const()[name = tensor("op_13577"), val = tensor([1, 1])]; + tensor var_13579 = const()[name = tensor("op_13579"), val = tensor([1, 1])]; + tensor q_275_pad_type_0 = const()[name = tensor("q_275_pad_type_0"), val = tensor("custom")]; + tensor q_275_pad_0 = const()[name = tensor("q_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1901758400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1902065664))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_275_cast_fp16 = conv(dilations = var_13579, groups = var_12518, pad = q_275_pad_0, pad_type = q_275_pad_type_0, strides = var_13577, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_563_cast_fp16)[name = tensor("q_275_cast_fp16")]; + tensor var_13583 = const()[name = tensor("op_13583"), val = tensor([1, 1])]; + tensor var_13585 = const()[name = tensor("op_13585"), val = tensor([1, 1])]; + tensor k_275_pad_type_0 = const()[name = tensor("k_275_pad_type_0"), val = tensor("custom")]; + tensor k_275_pad_0 = const()[name = tensor("k_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1902065856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1903048960))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_275_cast_fp16 = conv(dilations = var_13585, groups = var_12518, pad = k_275_pad_0, pad_type = k_275_pad_type_0, strides = var_13583, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_275_cast_fp16")]; + tensor var_13589 = const()[name = tensor("op_13589"), val = tensor([1, 1])]; + tensor var_13591 = const()[name = tensor("op_13591"), val = tensor([1, 1])]; + tensor v_275_pad_type_0 = const()[name = tensor("v_275_pad_type_0"), val = tensor("custom")]; + tensor v_275_pad_0 = const()[name = tensor("v_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1903049152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904032256))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_275_cast_fp16 = conv(dilations = var_13591, groups = var_12518, pad = v_275_pad_0, pad_type = v_275_pad_type_0, strides = var_13589, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_275_cast_fp16")]; + tensor var_13595 = const()[name = tensor("op_13595"), val = tensor([1, 10, 64, -1])]; + tensor var_13596_cast_fp16 = reshape(shape = var_13595, x = q_275_cast_fp16)[name = tensor("op_13596_cast_fp16")]; + tensor var_13597 = const()[name = tensor("op_13597"), val = tensor([1, 10, 64, -1])]; + tensor var_13598_cast_fp16 = reshape(shape = var_13597, x = k_275_cast_fp16)[name = tensor("op_13598_cast_fp16")]; + tensor var_13599 = const()[name = tensor("op_13599"), val = tensor([1, 10, 64, -1])]; + tensor var_13600_cast_fp16 = reshape(shape = var_13599, x = v_275_cast_fp16)[name = tensor("op_13600_cast_fp16")]; + tensor attn_weights_549_transpose_x_0 = const()[name = tensor("attn_weights_549_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_549_transpose_y_0 = const()[name = tensor("attn_weights_549_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_549_cast_fp16 = matmul(transpose_x = attn_weights_549_transpose_x_0, transpose_y = attn_weights_549_transpose_y_0, x = var_13596_cast_fp16, y = var_13598_cast_fp16)[name = tensor("attn_weights_549_cast_fp16")]; + tensor attn_weights_551_cast_fp16 = mul(x = attn_weights_549_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_551_cast_fp16")]; + tensor var_13604_cast_fp16 = softmax(axis = var_12502, x = attn_weights_551_cast_fp16)[name = tensor("op_13604_cast_fp16")]; + tensor attn_275_transpose_x_0 = const()[name = tensor("attn_275_transpose_x_0"), val = tensor(false)]; + tensor attn_275_transpose_y_0 = const()[name = tensor("attn_275_transpose_y_0"), val = tensor(true)]; + tensor attn_275_cast_fp16 = matmul(transpose_x = attn_275_transpose_x_0, transpose_y = attn_275_transpose_y_0, x = var_13600_cast_fp16, y = var_13604_cast_fp16)[name = tensor("attn_275_cast_fp16")]; + tensor var_13608 = const()[name = tensor("op_13608"), val = tensor([1, 640, 1, -1])]; + tensor input_807_cast_fp16 = reshape(shape = var_13608, x = attn_275_cast_fp16)[name = tensor("input_807_cast_fp16")]; + tensor var_13613 = const()[name = tensor("op_13613"), val = tensor([1, 1])]; + tensor var_13615 = const()[name = tensor("op_13615"), val = tensor([1, 1])]; + tensor var_13617_pad_type_0 = const()[name = tensor("op_13617_pad_type_0"), val = tensor("custom")]; + tensor var_13617_pad_0 = const()[name = tensor("op_13617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904032448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904339712))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904339904)))]; + tensor var_13617_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13615, groups = var_12518, pad = var_13617_pad_0, pad_type = var_13617_pad_type_0, strides = var_13613, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_807_cast_fp16)[name = tensor("op_13617_cast_fp16")]; + tensor inputs_413_cast_fp16 = add(x = var_13617_cast_fp16, y = inputs_411_cast_fp16)[name = tensor("inputs_413_cast_fp16")]; + tensor input_809_axes_0 = const()[name = tensor("input_809_axes_0"), val = tensor([1])]; + tensor input_809_gamma_0_to_fp16 = const()[name = tensor("input_809_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904341248)))]; + tensor input_809_beta_0_to_fp16 = const()[name = tensor("input_809_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904342592)))]; + tensor var_13627_to_fp16 = const()[name = tensor("op_13627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = input_809_beta_0_to_fp16, epsilon = var_13627_to_fp16, gamma = input_809_gamma_0_to_fp16, x = inputs_413_cast_fp16)[name = tensor("input_809_cast_fp16")]; + tensor var_13643 = const()[name = tensor("op_13643"), val = tensor([1, 1])]; + tensor var_13645 = const()[name = tensor("op_13645"), val = tensor([1, 1])]; + tensor var_13647_pad_type_0 = const()[name = tensor("op_13647_pad_type_0"), val = tensor("custom")]; + tensor var_13647_pad_0 = const()[name = tensor("op_13647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1904343936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1906801600))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1906801792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1906805696))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13647_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13645, groups = var_12518, pad = var_13647_pad_0, pad_type = var_13647_pad_type_0, strides = var_13643, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_809_cast_fp16)[name = tensor("op_13647_cast_fp16")]; + tensor var_13648_split_sizes_0 = const()[name = tensor("op_13648_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13648_axis_0 = const()[name = tensor("op_13648_axis_0"), val = tensor(1)]; + tensor var_13648_cast_fp16_0, tensor var_13648_cast_fp16_1 = split(axis = var_13648_axis_0, split_sizes = var_13648_split_sizes_0, x = var_13647_cast_fp16)[name = tensor("op_13648_cast_fp16")]; + tensor var_13650_mode_0 = const()[name = tensor("op_13650_mode_0"), val = tensor("EXACT")]; + tensor var_13650_cast_fp16 = gelu(mode = var_13650_mode_0, x = var_13648_cast_fp16_1)[name = tensor("op_13650_cast_fp16")]; + tensor input_811_cast_fp16 = mul(x = var_13648_cast_fp16_0, y = var_13650_cast_fp16)[name = tensor("input_811_cast_fp16")]; + tensor var_13654 = const()[name = tensor("op_13654"), val = tensor([1, 1])]; + tensor var_13656 = const()[name = tensor("op_13656"), val = tensor([1, 1])]; + tensor var_13658_pad_type_0 = const()[name = tensor("op_13658_pad_type_0"), val = tensor("custom")]; + tensor var_13658_pad_0 = const()[name = tensor("op_13658_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1906805888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908034752))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908034944)))]; + tensor var_13658_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13656, groups = var_12518, pad = var_13658_pad_0, pad_type = var_13658_pad_type_0, strides = var_13654, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_811_cast_fp16)[name = tensor("op_13658_cast_fp16")]; + tensor inputs_415_cast_fp16 = add(x = var_13658_cast_fp16, y = inputs_413_cast_fp16)[name = tensor("inputs_415_cast_fp16")]; + tensor hidden_states_567_axes_0 = const()[name = tensor("hidden_states_567_axes_0"), val = tensor([1])]; + tensor hidden_states_567_gamma_0_to_fp16 = const()[name = tensor("hidden_states_567_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908036288)))]; + tensor hidden_states_567_beta_0_to_fp16 = const()[name = tensor("hidden_states_567_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908037632)))]; + tensor var_13674_to_fp16 = const()[name = tensor("op_13674_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_567_cast_fp16 = layer_norm(axes = hidden_states_567_axes_0, beta = hidden_states_567_beta_0_to_fp16, epsilon = var_13674_to_fp16, gamma = hidden_states_567_gamma_0_to_fp16, x = inputs_415_cast_fp16)[name = tensor("hidden_states_567_cast_fp16")]; + tensor var_13689 = const()[name = tensor("op_13689"), val = tensor([1, 1])]; + tensor var_13691 = const()[name = tensor("op_13691"), val = tensor([1, 1])]; + tensor q_277_pad_type_0 = const()[name = tensor("q_277_pad_type_0"), val = tensor("custom")]; + tensor q_277_pad_0 = const()[name = tensor("q_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908038976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908346240))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_277_cast_fp16 = conv(dilations = var_13691, groups = var_12518, pad = q_277_pad_0, pad_type = q_277_pad_type_0, strides = var_13689, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_567_cast_fp16)[name = tensor("q_277_cast_fp16")]; + tensor var_13695 = const()[name = tensor("op_13695"), val = tensor([1, 1])]; + tensor var_13697 = const()[name = tensor("op_13697"), val = tensor([1, 1])]; + tensor k_277_pad_type_0 = const()[name = tensor("k_277_pad_type_0"), val = tensor("custom")]; + tensor k_277_pad_0 = const()[name = tensor("k_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908346432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908653696))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_277_cast_fp16 = conv(dilations = var_13697, groups = var_12518, pad = k_277_pad_0, pad_type = k_277_pad_type_0, strides = var_13695, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_567_cast_fp16)[name = tensor("k_277_cast_fp16")]; + tensor var_13701 = const()[name = tensor("op_13701"), val = tensor([1, 1])]; + tensor var_13703 = const()[name = tensor("op_13703"), val = tensor([1, 1])]; + tensor v_277_pad_type_0 = const()[name = tensor("v_277_pad_type_0"), val = tensor("custom")]; + tensor v_277_pad_0 = const()[name = tensor("v_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908653888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908961152))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_277_cast_fp16 = conv(dilations = var_13703, groups = var_12518, pad = v_277_pad_0, pad_type = v_277_pad_type_0, strides = var_13701, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_567_cast_fp16)[name = tensor("v_277_cast_fp16")]; + tensor var_13707 = const()[name = tensor("op_13707"), val = tensor([1, 10, 64, -1])]; + tensor var_13708_cast_fp16 = reshape(shape = var_13707, x = q_277_cast_fp16)[name = tensor("op_13708_cast_fp16")]; + tensor var_13709 = const()[name = tensor("op_13709"), val = tensor([1, 10, 64, -1])]; + tensor var_13710_cast_fp16 = reshape(shape = var_13709, x = k_277_cast_fp16)[name = tensor("op_13710_cast_fp16")]; + tensor var_13711 = const()[name = tensor("op_13711"), val = tensor([1, 10, 64, -1])]; + tensor var_13712_cast_fp16 = reshape(shape = var_13711, x = v_277_cast_fp16)[name = tensor("op_13712_cast_fp16")]; + tensor attn_weights_553_transpose_x_0 = const()[name = tensor("attn_weights_553_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_553_transpose_y_0 = const()[name = tensor("attn_weights_553_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_553_cast_fp16 = matmul(transpose_x = attn_weights_553_transpose_x_0, transpose_y = attn_weights_553_transpose_y_0, x = var_13708_cast_fp16, y = var_13710_cast_fp16)[name = tensor("attn_weights_553_cast_fp16")]; + tensor attn_weights_555_cast_fp16 = mul(x = attn_weights_553_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_555_cast_fp16")]; + tensor var_13716_cast_fp16 = softmax(axis = var_12502, x = attn_weights_555_cast_fp16)[name = tensor("op_13716_cast_fp16")]; + tensor attn_277_transpose_x_0 = const()[name = tensor("attn_277_transpose_x_0"), val = tensor(false)]; + tensor attn_277_transpose_y_0 = const()[name = tensor("attn_277_transpose_y_0"), val = tensor(true)]; + tensor attn_277_cast_fp16 = matmul(transpose_x = attn_277_transpose_x_0, transpose_y = attn_277_transpose_y_0, x = var_13712_cast_fp16, y = var_13716_cast_fp16)[name = tensor("attn_277_cast_fp16")]; + tensor var_13720 = const()[name = tensor("op_13720"), val = tensor([1, 640, 1, -1])]; + tensor input_813_cast_fp16 = reshape(shape = var_13720, x = attn_277_cast_fp16)[name = tensor("input_813_cast_fp16")]; + tensor var_13725 = const()[name = tensor("op_13725"), val = tensor([1, 1])]; + tensor var_13727 = const()[name = tensor("op_13727"), val = tensor([1, 1])]; + tensor var_13729_pad_type_0 = const()[name = tensor("op_13729_pad_type_0"), val = tensor("custom")]; + tensor var_13729_pad_0 = const()[name = tensor("op_13729_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1908961344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909268608))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909268800)))]; + tensor var_13729_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13727, groups = var_12518, pad = var_13729_pad_0, pad_type = var_13729_pad_type_0, strides = var_13725, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16_palettized, x = input_813_cast_fp16)[name = tensor("op_13729_cast_fp16")]; + tensor inputs_417_cast_fp16 = add(x = var_13729_cast_fp16, y = inputs_415_cast_fp16)[name = tensor("inputs_417_cast_fp16")]; + tensor hidden_states_569_axes_0 = const()[name = tensor("hidden_states_569_axes_0"), val = tensor([1])]; + tensor hidden_states_569_gamma_0_to_fp16 = const()[name = tensor("hidden_states_569_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909270144)))]; + tensor hidden_states_569_beta_0_to_fp16 = const()[name = tensor("hidden_states_569_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909271488)))]; + tensor var_13739_to_fp16 = const()[name = tensor("op_13739_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_569_cast_fp16 = layer_norm(axes = hidden_states_569_axes_0, beta = hidden_states_569_beta_0_to_fp16, epsilon = var_13739_to_fp16, gamma = hidden_states_569_gamma_0_to_fp16, x = inputs_417_cast_fp16)[name = tensor("hidden_states_569_cast_fp16")]; + tensor var_13754 = const()[name = tensor("op_13754"), val = tensor([1, 1])]; + tensor var_13756 = const()[name = tensor("op_13756"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909272832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909580096))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_cast_fp16 = conv(dilations = var_13756, groups = var_12518, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_13754, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_569_cast_fp16)[name = tensor("q_cast_fp16")]; + tensor var_13760 = const()[name = tensor("op_13760"), val = tensor([1, 1])]; + tensor var_13762 = const()[name = tensor("op_13762"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1909580288))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1910563392))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor k_cast_fp16 = conv(dilations = var_13762, groups = var_12518, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_13760, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_cast_fp16")]; + tensor var_13766 = const()[name = tensor("op_13766"), val = tensor([1, 1])]; + tensor var_13768 = const()[name = tensor("op_13768"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1910563584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911546688))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 2048, 1, 1])]; + tensor v_cast_fp16 = conv(dilations = var_13768, groups = var_12518, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_13766, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_cast_fp16")]; + tensor var_13772 = const()[name = tensor("op_13772"), val = tensor([1, 10, 64, -1])]; + tensor var_13773_cast_fp16 = reshape(shape = var_13772, x = q_cast_fp16)[name = tensor("op_13773_cast_fp16")]; + tensor var_13774 = const()[name = tensor("op_13774"), val = tensor([1, 10, 64, -1])]; + tensor var_13775_cast_fp16 = reshape(shape = var_13774, x = k_cast_fp16)[name = tensor("op_13775_cast_fp16")]; + tensor var_13776 = const()[name = tensor("op_13776"), val = tensor([1, 10, 64, -1])]; + tensor var_13777_cast_fp16 = reshape(shape = var_13776, x = v_cast_fp16)[name = tensor("op_13777_cast_fp16")]; + tensor attn_weights_557_transpose_x_0 = const()[name = tensor("attn_weights_557_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_557_transpose_y_0 = const()[name = tensor("attn_weights_557_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_557_cast_fp16 = matmul(transpose_x = attn_weights_557_transpose_x_0, transpose_y = attn_weights_557_transpose_y_0, x = var_13773_cast_fp16, y = var_13775_cast_fp16)[name = tensor("attn_weights_557_cast_fp16")]; + tensor attn_weights_cast_fp16 = mul(x = attn_weights_557_cast_fp16, y = var_12509_to_fp16)[name = tensor("attn_weights_cast_fp16")]; + tensor var_13781_cast_fp16 = softmax(axis = var_12502, x = attn_weights_cast_fp16)[name = tensor("op_13781_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_13777_cast_fp16, y = var_13781_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_13785 = const()[name = tensor("op_13785"), val = tensor([1, 640, 1, -1])]; + tensor input_815_cast_fp16 = reshape(shape = var_13785, x = attn_cast_fp16)[name = tensor("input_815_cast_fp16")]; + tensor var_13790 = const()[name = tensor("op_13790"), val = tensor([1, 1])]; + tensor var_13792 = const()[name = tensor("op_13792"), val = tensor([1, 1])]; + tensor var_13794_pad_type_0 = const()[name = tensor("op_13794_pad_type_0"), val = tensor("custom")]; + tensor var_13794_pad_0 = const()[name = tensor("op_13794_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911546880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911854144))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911854336)))]; + tensor var_13794_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13792, groups = var_12518, pad = var_13794_pad_0, pad_type = var_13794_pad_type_0, strides = var_13790, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16_palettized, x = input_815_cast_fp16)[name = tensor("op_13794_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = var_13794_cast_fp16, y = inputs_417_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor input_817_axes_0 = const()[name = tensor("input_817_axes_0"), val = tensor([1])]; + tensor input_817_gamma_0_to_fp16 = const()[name = tensor("input_817_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911855680)))]; + tensor input_817_beta_0_to_fp16 = const()[name = tensor("input_817_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911857024)))]; + tensor var_13804_to_fp16 = const()[name = tensor("op_13804_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_817_cast_fp16 = layer_norm(axes = input_817_axes_0, beta = input_817_beta_0_to_fp16, epsilon = var_13804_to_fp16, gamma = input_817_gamma_0_to_fp16, x = inputs_cast_fp16)[name = tensor("input_817_cast_fp16")]; + tensor var_13820 = const()[name = tensor("op_13820"), val = tensor([1, 1])]; + tensor var_13822 = const()[name = tensor("op_13822"), val = tensor([1, 1])]; + tensor var_13824_pad_type_0 = const()[name = tensor("op_13824_pad_type_0"), val = tensor("custom")]; + tensor var_13824_pad_0 = const()[name = tensor("op_13824_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1911858368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914316032))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914316224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914320128))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_13824_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_13822, groups = var_12518, pad = var_13824_pad_0, pad_type = var_13824_pad_type_0, strides = var_13820, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16_palettized, x = input_817_cast_fp16)[name = tensor("op_13824_cast_fp16")]; + tensor var_13825_split_sizes_0 = const()[name = tensor("op_13825_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13825_axis_0 = const()[name = tensor("op_13825_axis_0"), val = tensor(1)]; + tensor var_13825_cast_fp16_0, tensor var_13825_cast_fp16_1 = split(axis = var_13825_axis_0, split_sizes = var_13825_split_sizes_0, x = var_13824_cast_fp16)[name = tensor("op_13825_cast_fp16")]; + tensor var_13827_mode_0 = const()[name = tensor("op_13827_mode_0"), val = tensor("EXACT")]; + tensor var_13827_cast_fp16 = gelu(mode = var_13827_mode_0, x = var_13825_cast_fp16_1)[name = tensor("op_13827_cast_fp16")]; + tensor input_819_cast_fp16 = mul(x = var_13825_cast_fp16_0, y = var_13827_cast_fp16)[name = tensor("input_819_cast_fp16")]; + tensor var_13831 = const()[name = tensor("op_13831"), val = tensor([1, 1])]; + tensor var_13833 = const()[name = tensor("op_13833"), val = tensor([1, 1])]; + tensor var_13835_pad_type_0 = const()[name = tensor("op_13835_pad_type_0"), val = tensor("custom")]; + tensor var_13835_pad_0 = const()[name = tensor("op_13835_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1914320320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915549184))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915549376)))]; + tensor var_13835_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13833, groups = var_12518, pad = var_13835_pad_0, pad_type = var_13835_pad_type_0, strides = var_13831, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16_palettized, x = input_819_cast_fp16)[name = tensor("op_13835_cast_fp16")]; + tensor hidden_states_573_cast_fp16 = add(x = var_13835_cast_fp16, y = inputs_cast_fp16)[name = tensor("hidden_states_573_cast_fp16")]; + tensor var_13837 = const()[name = tensor("op_13837"), val = tensor([1, 640, 64, 64])]; + tensor input_821_cast_fp16 = reshape(shape = var_13837, x = hidden_states_573_cast_fp16)[name = tensor("input_821_cast_fp16")]; + tensor var_13841 = const()[name = tensor("op_13841"), val = tensor([1, 1])]; + tensor var_13843 = const()[name = tensor("op_13843"), val = tensor([1, 1])]; + tensor hidden_states_575_pad_type_0 = const()[name = tensor("hidden_states_575_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_575_pad_0 = const()[name = tensor("hidden_states_575_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915550720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915857984))), name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915858176)))]; + tensor hidden_states_575_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_13843, groups = var_12518, pad = hidden_states_575_pad_0, pad_type = hidden_states_575_pad_type_0, strides = var_13841, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_821_cast_fp16)[name = tensor("hidden_states_575_cast_fp16")]; + tensor input_823_cast_fp16 = add(x = hidden_states_575_cast_fp16, y = hidden_states_557_cast_fp16)[name = tensor("input_823_cast_fp16")]; + tensor input_825_scale_factor_height_0 = const()[name = tensor("input_825_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_825_scale_factor_width_0 = const()[name = tensor("input_825_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_825_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_825_scale_factor_height_0, scale_factor_width = input_825_scale_factor_width_0, x = input_823_cast_fp16)[name = tensor("input_825_cast_fp16")]; + tensor var_13852 = const()[name = tensor("op_13852"), val = tensor([1, 1])]; + tensor var_13854 = const()[name = tensor("op_13854"), val = tensor([1, 1])]; + tensor hidden_states_577_pad_type_0 = const()[name = tensor("hidden_states_577_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_577_pad_0 = const()[name = tensor("hidden_states_577_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915859520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1918624384))), name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1918624576)))]; + tensor hidden_states_577_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_13854, groups = var_12518, pad = hidden_states_577_pad_0, pad_type = hidden_states_577_pad_type_0, strides = var_13852, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized, x = input_825_cast_fp16)[name = tensor("hidden_states_577_cast_fp16")]; + tensor var_13862 = const()[name = tensor("op_13862"), val = tensor(1)]; + tensor input_827_interleave_0 = const()[name = tensor("input_827_interleave_0"), val = tensor(false)]; + tensor input_827_cast_fp16 = concat(axis = var_13862, interleave = input_827_interleave_0, values = (hidden_states_577_cast_fp16, input_43_cast_fp16))[name = tensor("input_827_cast_fp16")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([1, 32, 30, 128, 128])]; + tensor reshape_156_cast_fp16 = reshape(shape = reshape_156_shape_0, x = input_827_cast_fp16)[name = tensor("reshape_156_cast_fp16")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast_fp16)[name = tensor("reduce_mean_117_cast_fp16")]; + tensor sub_78_cast_fp16 = sub(x = reshape_156_cast_fp16, y = reduce_mean_117_cast_fp16)[name = tensor("sub_78_cast_fp16")]; + tensor square_39_cast_fp16 = square(x = sub_78_cast_fp16)[name = tensor("square_39_cast_fp16")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast_fp16)[name = tensor("reduce_mean_119_cast_fp16")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast_fp16 = add(x = reduce_mean_119_cast_fp16, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast_fp16")]; + tensor sqrt_39_cast_fp16 = sqrt(x = add_78_cast_fp16)[name = tensor("sqrt_39_cast_fp16")]; + tensor real_div_39_cast_fp16 = real_div(x = sub_78_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor("real_div_39_cast_fp16")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([1, 960, 128, 128])]; + tensor reshape_157_cast_fp16 = reshape(shape = reshape_157_shape_0, x = real_div_39_cast_fp16)[name = tensor("reshape_157_cast_fp16")]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1918625920)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1918627904)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast_fp16 = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_157_cast_fp16)[name = tensor("add_79_cast_fp16")]; + tensor input_831_cast_fp16 = silu(x = add_79_cast_fp16)[name = tensor("input_831_cast_fp16")]; + tensor var_13883 = const()[name = tensor("op_13883"), val = tensor([1, 1])]; + tensor var_13885 = const()[name = tensor("op_13885"), val = tensor([1, 1])]; + tensor hidden_states_579_pad_type_0 = const()[name = tensor("hidden_states_579_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_579_pad_0 = const()[name = tensor("hidden_states_579_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1918629888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1920703552))), name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 960, 3, 3])]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1920703744)))]; + tensor hidden_states_579_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_13885, groups = var_13862, pad = hidden_states_579_pad_0, pad_type = hidden_states_579_pad_type_0, strides = var_13883, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_831_cast_fp16)[name = tensor("hidden_states_579_cast_fp16")]; + tensor var_13891 = const()[name = tensor("op_13891"), val = tensor([1, 1])]; + tensor var_13893 = const()[name = tensor("op_13893"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1920704448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921011712))), name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921011904)))]; + tensor temb_29_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_13893, groups = var_13862, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_13891, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_29_cast_fp16")]; + tensor input_835_cast_fp16 = add(x = hidden_states_579_cast_fp16, y = temb_29_cast_fp16)[name = tensor("input_835_cast_fp16")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_160_cast_fp16 = reshape(shape = reshape_160_shape_0, x = input_835_cast_fp16)[name = tensor("reshape_160_cast_fp16")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast_fp16 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast_fp16)[name = tensor("reduce_mean_120_cast_fp16")]; + tensor sub_80_cast_fp16 = sub(x = reshape_160_cast_fp16, y = reduce_mean_120_cast_fp16)[name = tensor("sub_80_cast_fp16")]; + tensor square_40_cast_fp16 = square(x = sub_80_cast_fp16)[name = tensor("square_40_cast_fp16")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast_fp16 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast_fp16)[name = tensor("reduce_mean_122_cast_fp16")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast_fp16 = add(x = reduce_mean_122_cast_fp16, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast_fp16")]; + tensor sqrt_40_cast_fp16 = sqrt(x = add_80_cast_fp16)[name = tensor("sqrt_40_cast_fp16")]; + tensor real_div_40_cast_fp16 = real_div(x = sub_80_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor("real_div_40_cast_fp16")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_161_cast_fp16 = reshape(shape = reshape_161_shape_0, x = real_div_40_cast_fp16)[name = tensor("reshape_161_cast_fp16")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921012608)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921013312)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast_fp16 = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_161_cast_fp16)[name = tensor("add_81_cast_fp16")]; + tensor input_839_cast_fp16 = silu(x = add_81_cast_fp16)[name = tensor("input_839_cast_fp16")]; + tensor var_13903 = const()[name = tensor("op_13903"), val = tensor([1, 1])]; + tensor var_13905 = const()[name = tensor("op_13905"), val = tensor([1, 1])]; + tensor hidden_states_581_pad_type_0 = const()[name = tensor("hidden_states_581_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_581_pad_0 = const()[name = tensor("hidden_states_581_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921014016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921705280))), name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921705472)))]; + tensor hidden_states_581_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_13905, groups = var_13862, pad = hidden_states_581_pad_0, pad_type = hidden_states_581_pad_type_0, strides = var_13903, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_839_cast_fp16)[name = tensor("hidden_states_581_cast_fp16")]; + tensor var_13910 = const()[name = tensor("op_13910"), val = tensor([1, 1])]; + tensor var_13912 = const()[name = tensor("op_13912"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921706176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921936640))), name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 960, 1, 1])]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921936832)))]; + tensor x_17_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_13912, groups = var_13862, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_13910, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_827_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor hidden_states_583_cast_fp16 = add(x = x_17_cast_fp16, y = hidden_states_581_cast_fp16)[name = tensor("hidden_states_583_cast_fp16")]; + tensor input_841_interleave_0 = const()[name = tensor("input_841_interleave_0"), val = tensor(false)]; + tensor input_841_cast_fp16 = concat(axis = var_13862, interleave = input_841_interleave_0, values = (hidden_states_583_cast_fp16, input_29_cast_fp16))[name = tensor("input_841_cast_fp16")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([1, 32, 20, 128, 128])]; + tensor reshape_164_cast_fp16 = reshape(shape = reshape_164_shape_0, x = input_841_cast_fp16)[name = tensor("reshape_164_cast_fp16")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast_fp16)[name = tensor("reduce_mean_123_cast_fp16")]; + tensor sub_82_cast_fp16 = sub(x = reshape_164_cast_fp16, y = reduce_mean_123_cast_fp16)[name = tensor("sub_82_cast_fp16")]; + tensor square_41_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor("square_41_cast_fp16")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast_fp16)[name = tensor("reduce_mean_125_cast_fp16")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_82_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast_fp16")]; + tensor sqrt_41_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor("sqrt_41_cast_fp16")]; + tensor real_div_41_cast_fp16 = real_div(x = sub_82_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor("real_div_41_cast_fp16")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([1, 640, 128, 128])]; + tensor reshape_165_cast_fp16 = reshape(shape = reshape_165_shape_0, x = real_div_41_cast_fp16)[name = tensor("reshape_165_cast_fp16")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921937536)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921938880)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast_fp16 = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_165_cast_fp16)[name = tensor("add_83_cast_fp16")]; + tensor input_845_cast_fp16 = silu(x = add_83_cast_fp16)[name = tensor("input_845_cast_fp16")]; + tensor var_13930 = const()[name = tensor("op_13930"), val = tensor([1, 1])]; + tensor var_13932 = const()[name = tensor("op_13932"), val = tensor([1, 1])]; + tensor hidden_states_585_pad_type_0 = const()[name = tensor("hidden_states_585_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_585_pad_0 = const()[name = tensor("hidden_states_585_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921940224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923322688))), name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923322880)))]; + tensor hidden_states_585_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_13932, groups = var_13862, pad = hidden_states_585_pad_0, pad_type = hidden_states_585_pad_type_0, strides = var_13930, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_845_cast_fp16)[name = tensor("hidden_states_585_cast_fp16")]; + tensor var_13938 = const()[name = tensor("op_13938"), val = tensor([1, 1])]; + tensor var_13940 = const()[name = tensor("op_13940"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923323584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923630848))), name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923631040)))]; + tensor temb_31_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_13940, groups = var_13862, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_13938, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_31_cast_fp16")]; + tensor input_849_cast_fp16 = add(x = hidden_states_585_cast_fp16, y = temb_31_cast_fp16)[name = tensor("input_849_cast_fp16")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_168_cast_fp16 = reshape(shape = reshape_168_shape_0, x = input_849_cast_fp16)[name = tensor("reshape_168_cast_fp16")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast_fp16)[name = tensor("reduce_mean_126_cast_fp16")]; + tensor sub_84_cast_fp16 = sub(x = reshape_168_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor("sub_84_cast_fp16")]; + tensor square_42_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor("square_42_cast_fp16")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast_fp16)[name = tensor("reduce_mean_128_cast_fp16")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast_fp16 = add(x = reduce_mean_128_cast_fp16, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast_fp16")]; + tensor sqrt_42_cast_fp16 = sqrt(x = add_84_cast_fp16)[name = tensor("sqrt_42_cast_fp16")]; + tensor real_div_42_cast_fp16 = real_div(x = sub_84_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor("real_div_42_cast_fp16")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_169_cast_fp16 = reshape(shape = reshape_169_shape_0, x = real_div_42_cast_fp16)[name = tensor("reshape_169_cast_fp16")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923631744)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923632448)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast_fp16 = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_169_cast_fp16)[name = tensor("add_85_cast_fp16")]; + tensor input_853_cast_fp16 = silu(x = add_85_cast_fp16)[name = tensor("input_853_cast_fp16")]; + tensor var_13950 = const()[name = tensor("op_13950"), val = tensor([1, 1])]; + tensor var_13952 = const()[name = tensor("op_13952"), val = tensor([1, 1])]; + tensor hidden_states_587_pad_type_0 = const()[name = tensor("hidden_states_587_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_587_pad_0 = const()[name = tensor("hidden_states_587_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1923633152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924324416))), name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924324608)))]; + tensor hidden_states_587_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_13952, groups = var_13862, pad = hidden_states_587_pad_0, pad_type = hidden_states_587_pad_type_0, strides = var_13950, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_853_cast_fp16)[name = tensor("hidden_states_587_cast_fp16")]; + tensor var_13957 = const()[name = tensor("op_13957"), val = tensor([1, 1])]; + tensor var_13959 = const()[name = tensor("op_13959"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924325312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924478976))), name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924479168)))]; + tensor x_19_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13959, groups = var_13862, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_13957, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_841_cast_fp16)[name = tensor("x_19_cast_fp16")]; + tensor hidden_states_589_cast_fp16 = add(x = x_19_cast_fp16, y = hidden_states_587_cast_fp16)[name = tensor("hidden_states_589_cast_fp16")]; + tensor input_855_interleave_0 = const()[name = tensor("input_855_interleave_0"), val = tensor(false)]; + tensor input_855_cast_fp16 = concat(axis = var_13862, interleave = input_855_interleave_0, values = (hidden_states_589_cast_fp16, input_13_cast_fp16))[name = tensor("input_855_cast_fp16")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([1, 32, 20, 128, 128])]; + tensor reshape_172_cast_fp16 = reshape(shape = reshape_172_shape_0, x = input_855_cast_fp16)[name = tensor("reshape_172_cast_fp16")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast_fp16)[name = tensor("reduce_mean_129_cast_fp16")]; + tensor sub_86_cast_fp16 = sub(x = reshape_172_cast_fp16, y = reduce_mean_129_cast_fp16)[name = tensor("sub_86_cast_fp16")]; + tensor square_43_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor("square_43_cast_fp16")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast_fp16)[name = tensor("reduce_mean_131_cast_fp16")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast_fp16 = add(x = reduce_mean_131_cast_fp16, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast_fp16")]; + tensor sqrt_43_cast_fp16 = sqrt(x = add_86_cast_fp16)[name = tensor("sqrt_43_cast_fp16")]; + tensor real_div_43_cast_fp16 = real_div(x = sub_86_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor("real_div_43_cast_fp16")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([1, 640, 128, 128])]; + tensor reshape_173_cast_fp16 = reshape(shape = reshape_173_shape_0, x = real_div_43_cast_fp16)[name = tensor("reshape_173_cast_fp16")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924479872)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924481216)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast_fp16 = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_173_cast_fp16)[name = tensor("add_87_cast_fp16")]; + tensor input_859_cast_fp16 = silu(x = add_87_cast_fp16)[name = tensor("input_859_cast_fp16")]; + tensor var_13977 = const()[name = tensor("op_13977"), val = tensor([1, 1])]; + tensor var_13979 = const()[name = tensor("op_13979"), val = tensor([1, 1])]; + tensor hidden_states_591_pad_type_0 = const()[name = tensor("hidden_states_591_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_591_pad_0 = const()[name = tensor("hidden_states_591_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1924482560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1925865024))), name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1925865216)))]; + tensor hidden_states_591_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_13979, groups = var_13862, pad = hidden_states_591_pad_0, pad_type = hidden_states_591_pad_type_0, strides = var_13977, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized, x = input_859_cast_fp16)[name = tensor("hidden_states_591_cast_fp16")]; + tensor var_13985 = const()[name = tensor("op_13985"), val = tensor([1, 1])]; + tensor var_13987 = const()[name = tensor("op_13987"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1925865920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926173184))), name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926173376)))]; + tensor temb_cast_fp16 = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13987, groups = var_13862, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_13985, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor("temb_cast_fp16")]; + tensor input_863_cast_fp16 = add(x = hidden_states_591_cast_fp16, y = temb_cast_fp16)[name = tensor("input_863_cast_fp16")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_176_cast_fp16 = reshape(shape = reshape_176_shape_0, x = input_863_cast_fp16)[name = tensor("reshape_176_cast_fp16")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast_fp16 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast_fp16)[name = tensor("reduce_mean_132_cast_fp16")]; + tensor sub_88_cast_fp16 = sub(x = reshape_176_cast_fp16, y = reduce_mean_132_cast_fp16)[name = tensor("sub_88_cast_fp16")]; + tensor square_44_cast_fp16 = square(x = sub_88_cast_fp16)[name = tensor("square_44_cast_fp16")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast_fp16 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast_fp16)[name = tensor("reduce_mean_134_cast_fp16")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_88_cast_fp16 = add(x = reduce_mean_134_cast_fp16, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast_fp16")]; + tensor sqrt_44_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor("sqrt_44_cast_fp16")]; + tensor real_div_44_cast_fp16 = real_div(x = sub_88_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor("real_div_44_cast_fp16")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_177_cast_fp16 = reshape(shape = reshape_177_shape_0, x = real_div_44_cast_fp16)[name = tensor("reshape_177_cast_fp16")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926174080)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926174784)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast_fp16 = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_177_cast_fp16)[name = tensor("add_89_cast_fp16")]; + tensor input_867_cast_fp16 = silu(x = add_89_cast_fp16)[name = tensor("input_867_cast_fp16")]; + tensor var_13997 = const()[name = tensor("op_13997"), val = tensor([1, 1])]; + tensor var_13999 = const()[name = tensor("op_13999"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926175488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926866752))), name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926866944)))]; + tensor hidden_states_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_13999, groups = var_13862, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_13997, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized, x = input_867_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor var_14004 = const()[name = tensor("op_14004"), val = tensor([1, 1])]; + tensor var_14006 = const()[name = tensor("op_14006"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1926867648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927021312))), name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927021504)))]; + tensor x_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_14006, groups = var_13862, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_14004, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_855_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor input_869_cast_fp16 = add(x = x_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("input_869_cast_fp16")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([1, 32, 10, 128, 128])]; + tensor reshape_180_cast_fp16 = reshape(shape = reshape_180_shape_0, x = input_869_cast_fp16)[name = tensor("reshape_180_cast_fp16")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast_fp16)[name = tensor("reduce_mean_135_cast_fp16")]; + tensor sub_90_cast_fp16 = sub(x = reshape_180_cast_fp16, y = reduce_mean_135_cast_fp16)[name = tensor("sub_90_cast_fp16")]; + tensor square_45_cast_fp16 = square(x = sub_90_cast_fp16)[name = tensor("square_45_cast_fp16")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast_fp16)[name = tensor("reduce_mean_137_cast_fp16")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast_fp16 = add(x = reduce_mean_137_cast_fp16, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast_fp16")]; + tensor sqrt_45_cast_fp16 = sqrt(x = add_90_cast_fp16)[name = tensor("sqrt_45_cast_fp16")]; + tensor real_div_45_cast_fp16 = real_div(x = sub_90_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor("real_div_45_cast_fp16")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([1, 320, 128, 128])]; + tensor reshape_181_cast_fp16 = reshape(shape = reshape_181_shape_0, x = real_div_45_cast_fp16)[name = tensor("reshape_181_cast_fp16")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927022208)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927022912)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast_fp16 = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_181_cast_fp16)[name = tensor("add_91_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_91_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_14020 = const()[name = tensor("op_14020"), val = tensor(1)]; + tensor var_14023 = const()[name = tensor("op_14023"), val = tensor([1, 1])]; + tensor var_14025 = const()[name = tensor("op_14025"), val = tensor([1, 1])]; + tensor var_14027_pad_type_0 = const()[name = tensor("op_14027_pad_type_0"), val = tensor("custom")]; + tensor var_14027_pad_0 = const()[name = tensor("op_14027_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927023616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1927032320))), name = tensor("conv_out_weight_to_fp16_palettized"), shape = tensor([4, 320, 3, 3])]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([0x1.d0cp-7, 0x1.9e4p-11, 0x1.7dp-9, -0x1.364p-9])]; + tensor var_14027_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_14025, groups = var_14020, pad = var_14027_pad_0, pad_type = var_14027_pad_type_0, strides = var_14023, weight = conv_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = tensor("op_14027_cast_fp16")]; + tensor var_14027_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_14027_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_14027_cast_fp16_to_fp32_dtype_0, x = var_14027_cast_fp16)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file