CifNetForImageClassification(
  (resnet): CifNetModel(
    (embedder): CifNetEmbeddings(
      (embedder): CifNetConvLayer(
        (convolution): Conv2d(3, 16, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (activation): SiLU()
      )
      (pooler): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (encoder): CifNetEncoder(
      (stages): ModuleList(
        (0): CifNetStage(
          (layers): Sequential(
            (0): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (1): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (2): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (3): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (4): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (5): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (6): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (7): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (8): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (9): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (10): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (11): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (12): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (13): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (14): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (15): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (16): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (17): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
          )
        )
        (1): CifNetStage(
          (layers): Sequential(
            (0): CifNetBasicLayer(
              (shortcut): CifNetShortCut(
                (convolution): Conv2d(16, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)
                (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (1): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (2): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (3): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (4): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (5): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (6): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (7): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (8): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (9): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (10): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (11): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (12): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (13): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (14): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (15): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (16): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (17): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
          )
        )
        (2): CifNetStage(
          (layers): Sequential(
            (0): CifNetBasicLayer(
              (shortcut): CifNetShortCut(
                (convolution): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
                (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (1): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (2): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (3): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (4): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (5): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (6): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (7): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (8): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (9): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (10): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (11): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (12): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (13): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (14): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (15): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (16): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (17): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
          )
        )
      )
    )
    (pooler): AdaptiveAvgPool2d(output_size=(1, 1))
  )
  (classifier): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=64, out_features=10, bias=True)
  )
)
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1          [4, 16, 112, 112]           2,352
       BatchNorm2d-2          [4, 16, 112, 112]              32
              SiLU-3          [4, 16, 112, 112]               0
   CifNetConvLayer-4          [4, 16, 112, 112]               0
         MaxPool2d-5            [4, 16, 56, 56]               0
  CifNetEmbeddings-6            [4, 16, 56, 56]               0
            Conv2d-7            [4, 16, 56, 56]           2,304
       BatchNorm2d-8            [4, 16, 56, 56]              32
              SiLU-9            [4, 16, 56, 56]               0
  CifNetConvLayer-10            [4, 16, 56, 56]               0
           Conv2d-11            [4, 16, 56, 56]           2,304
      BatchNorm2d-12            [4, 16, 56, 56]              32
             SiLU-13            [4, 16, 56, 56]               0
  CifNetConvLayer-14            [4, 16, 56, 56]               0
         Identity-15            [4, 16, 56, 56]               0
 CifNetBasicLayer-16            [4, 16, 56, 56]               0
           Conv2d-17            [4, 16, 56, 56]           2,304
      BatchNorm2d-18            [4, 16, 56, 56]              32
             SiLU-19            [4, 16, 56, 56]               0
  CifNetConvLayer-20            [4, 16, 56, 56]               0
           Conv2d-21            [4, 16, 56, 56]           2,304
      BatchNorm2d-22            [4, 16, 56, 56]              32
             SiLU-23            [4, 16, 56, 56]               0
  CifNetConvLayer-24            [4, 16, 56, 56]               0
         Identity-25            [4, 16, 56, 56]               0
 CifNetBasicLayer-26            [4, 16, 56, 56]               0
           Conv2d-27            [4, 16, 56, 56]           2,304
      BatchNorm2d-28            [4, 16, 56, 56]              32
             SiLU-29            [4, 16, 56, 56]               0
  CifNetConvLayer-30            [4, 16, 56, 56]               0
           Conv2d-31            [4, 16, 56, 56]           2,304
      BatchNorm2d-32            [4, 16, 56, 56]              32
             SiLU-33            [4, 16, 56, 56]               0
  CifNetConvLayer-34            [4, 16, 56, 56]               0
         Identity-35            [4, 16, 56, 56]               0
 CifNetBasicLayer-36            [4, 16, 56, 56]               0
           Conv2d-37            [4, 16, 56, 56]           2,304
      BatchNorm2d-38            [4, 16, 56, 56]              32
             SiLU-39            [4, 16, 56, 56]               0
  CifNetConvLayer-40            [4, 16, 56, 56]               0
           Conv2d-41            [4, 16, 56, 56]           2,304
      BatchNorm2d-42            [4, 16, 56, 56]              32
             SiLU-43            [4, 16, 56, 56]               0
  CifNetConvLayer-44            [4, 16, 56, 56]               0
         Identity-45            [4, 16, 56, 56]               0
 CifNetBasicLayer-46            [4, 16, 56, 56]               0
           Conv2d-47            [4, 16, 56, 56]           2,304
      BatchNorm2d-48            [4, 16, 56, 56]              32
             SiLU-49            [4, 16, 56, 56]               0
  CifNetConvLayer-50            [4, 16, 56, 56]               0
           Conv2d-51            [4, 16, 56, 56]           2,304
      BatchNorm2d-52            [4, 16, 56, 56]              32
             SiLU-53            [4, 16, 56, 56]               0
  CifNetConvLayer-54            [4, 16, 56, 56]               0
         Identity-55            [4, 16, 56, 56]               0
 CifNetBasicLayer-56            [4, 16, 56, 56]               0
           Conv2d-57            [4, 16, 56, 56]           2,304
      BatchNorm2d-58            [4, 16, 56, 56]              32
             SiLU-59            [4, 16, 56, 56]               0
  CifNetConvLayer-60            [4, 16, 56, 56]               0
           Conv2d-61            [4, 16, 56, 56]           2,304
      BatchNorm2d-62            [4, 16, 56, 56]              32
             SiLU-63            [4, 16, 56, 56]               0
  CifNetConvLayer-64            [4, 16, 56, 56]               0
         Identity-65            [4, 16, 56, 56]               0
 CifNetBasicLayer-66            [4, 16, 56, 56]               0
           Conv2d-67            [4, 16, 56, 56]           2,304
      BatchNorm2d-68            [4, 16, 56, 56]              32
             SiLU-69            [4, 16, 56, 56]               0
  CifNetConvLayer-70            [4, 16, 56, 56]               0
           Conv2d-71            [4, 16, 56, 56]           2,304
      BatchNorm2d-72            [4, 16, 56, 56]              32
             SiLU-73            [4, 16, 56, 56]               0
  CifNetConvLayer-74            [4, 16, 56, 56]               0
         Identity-75            [4, 16, 56, 56]               0
 CifNetBasicLayer-76            [4, 16, 56, 56]               0
           Conv2d-77            [4, 16, 56, 56]           2,304
      BatchNorm2d-78            [4, 16, 56, 56]              32
             SiLU-79            [4, 16, 56, 56]               0
  CifNetConvLayer-80            [4, 16, 56, 56]               0
           Conv2d-81            [4, 16, 56, 56]           2,304
      BatchNorm2d-82            [4, 16, 56, 56]              32
             SiLU-83            [4, 16, 56, 56]               0
  CifNetConvLayer-84            [4, 16, 56, 56]               0
         Identity-85            [4, 16, 56, 56]               0
 CifNetBasicLayer-86            [4, 16, 56, 56]               0
           Conv2d-87            [4, 16, 56, 56]           2,304
      BatchNorm2d-88            [4, 16, 56, 56]              32
             SiLU-89            [4, 16, 56, 56]               0
  CifNetConvLayer-90            [4, 16, 56, 56]               0
           Conv2d-91            [4, 16, 56, 56]           2,304
      BatchNorm2d-92            [4, 16, 56, 56]              32
             SiLU-93            [4, 16, 56, 56]               0
  CifNetConvLayer-94            [4, 16, 56, 56]               0
         Identity-95            [4, 16, 56, 56]               0
 CifNetBasicLayer-96            [4, 16, 56, 56]               0
           Conv2d-97            [4, 16, 56, 56]           2,304
      BatchNorm2d-98            [4, 16, 56, 56]              32
             SiLU-99            [4, 16, 56, 56]               0
 CifNetConvLayer-100            [4, 16, 56, 56]               0
          Conv2d-101            [4, 16, 56, 56]           2,304
     BatchNorm2d-102            [4, 16, 56, 56]              32
            SiLU-103            [4, 16, 56, 56]               0
 CifNetConvLayer-104            [4, 16, 56, 56]               0
        Identity-105            [4, 16, 56, 56]               0
CifNetBasicLayer-106            [4, 16, 56, 56]               0
          Conv2d-107            [4, 16, 56, 56]           2,304
     BatchNorm2d-108            [4, 16, 56, 56]              32
            SiLU-109            [4, 16, 56, 56]               0
 CifNetConvLayer-110            [4, 16, 56, 56]               0
          Conv2d-111            [4, 16, 56, 56]           2,304
     BatchNorm2d-112            [4, 16, 56, 56]              32
            SiLU-113            [4, 16, 56, 56]               0
 CifNetConvLayer-114            [4, 16, 56, 56]               0
        Identity-115            [4, 16, 56, 56]               0
CifNetBasicLayer-116            [4, 16, 56, 56]               0
          Conv2d-117            [4, 16, 56, 56]           2,304
     BatchNorm2d-118            [4, 16, 56, 56]              32
            SiLU-119            [4, 16, 56, 56]               0
 CifNetConvLayer-120            [4, 16, 56, 56]               0
          Conv2d-121            [4, 16, 56, 56]           2,304
     BatchNorm2d-122            [4, 16, 56, 56]              32
            SiLU-123            [4, 16, 56, 56]               0
 CifNetConvLayer-124            [4, 16, 56, 56]               0
        Identity-125            [4, 16, 56, 56]               0
CifNetBasicLayer-126            [4, 16, 56, 56]               0
          Conv2d-127            [4, 16, 56, 56]           2,304
     BatchNorm2d-128            [4, 16, 56, 56]              32
            SiLU-129            [4, 16, 56, 56]               0
 CifNetConvLayer-130            [4, 16, 56, 56]               0
          Conv2d-131            [4, 16, 56, 56]           2,304
     BatchNorm2d-132            [4, 16, 56, 56]              32
            SiLU-133            [4, 16, 56, 56]               0
 CifNetConvLayer-134            [4, 16, 56, 56]               0
        Identity-135            [4, 16, 56, 56]               0
CifNetBasicLayer-136            [4, 16, 56, 56]               0
          Conv2d-137            [4, 16, 56, 56]           2,304
     BatchNorm2d-138            [4, 16, 56, 56]              32
            SiLU-139            [4, 16, 56, 56]               0
 CifNetConvLayer-140            [4, 16, 56, 56]               0
          Conv2d-141            [4, 16, 56, 56]           2,304
     BatchNorm2d-142            [4, 16, 56, 56]              32
            SiLU-143            [4, 16, 56, 56]               0
 CifNetConvLayer-144            [4, 16, 56, 56]               0
        Identity-145            [4, 16, 56, 56]               0
CifNetBasicLayer-146            [4, 16, 56, 56]               0
          Conv2d-147            [4, 16, 56, 56]           2,304
     BatchNorm2d-148            [4, 16, 56, 56]              32
            SiLU-149            [4, 16, 56, 56]               0
 CifNetConvLayer-150            [4, 16, 56, 56]               0
          Conv2d-151            [4, 16, 56, 56]           2,304
     BatchNorm2d-152            [4, 16, 56, 56]              32
            SiLU-153            [4, 16, 56, 56]               0
 CifNetConvLayer-154            [4, 16, 56, 56]               0
        Identity-155            [4, 16, 56, 56]               0
CifNetBasicLayer-156            [4, 16, 56, 56]               0
          Conv2d-157            [4, 16, 56, 56]           2,304
     BatchNorm2d-158            [4, 16, 56, 56]              32
            SiLU-159            [4, 16, 56, 56]               0
 CifNetConvLayer-160            [4, 16, 56, 56]               0
          Conv2d-161            [4, 16, 56, 56]           2,304
     BatchNorm2d-162            [4, 16, 56, 56]              32
            SiLU-163            [4, 16, 56, 56]               0
 CifNetConvLayer-164            [4, 16, 56, 56]               0
        Identity-165            [4, 16, 56, 56]               0
CifNetBasicLayer-166            [4, 16, 56, 56]               0
          Conv2d-167            [4, 16, 56, 56]           2,304
     BatchNorm2d-168            [4, 16, 56, 56]              32
            SiLU-169            [4, 16, 56, 56]               0
 CifNetConvLayer-170            [4, 16, 56, 56]               0
          Conv2d-171            [4, 16, 56, 56]           2,304
     BatchNorm2d-172            [4, 16, 56, 56]              32
            SiLU-173            [4, 16, 56, 56]               0
 CifNetConvLayer-174            [4, 16, 56, 56]               0
        Identity-175            [4, 16, 56, 56]               0
CifNetBasicLayer-176            [4, 16, 56, 56]               0
          Conv2d-177            [4, 16, 56, 56]           2,304
     BatchNorm2d-178            [4, 16, 56, 56]              32
            SiLU-179            [4, 16, 56, 56]               0
 CifNetConvLayer-180            [4, 16, 56, 56]               0
          Conv2d-181            [4, 16, 56, 56]           2,304
     BatchNorm2d-182            [4, 16, 56, 56]              32
            SiLU-183            [4, 16, 56, 56]               0
 CifNetConvLayer-184            [4, 16, 56, 56]               0
        Identity-185            [4, 16, 56, 56]               0
CifNetBasicLayer-186            [4, 16, 56, 56]               0
     CifNetStage-187            [4, 16, 56, 56]               0
          Conv2d-188            [4, 32, 28, 28]           4,608
     BatchNorm2d-189            [4, 32, 28, 28]              64
            SiLU-190            [4, 32, 28, 28]               0
 CifNetConvLayer-191            [4, 32, 28, 28]               0
          Conv2d-192            [4, 32, 28, 28]           9,216
     BatchNorm2d-193            [4, 32, 28, 28]              64
            SiLU-194            [4, 32, 28, 28]               0
 CifNetConvLayer-195            [4, 32, 28, 28]               0
          Conv2d-196            [4, 32, 28, 28]             512
     BatchNorm2d-197            [4, 32, 28, 28]              64
  CifNetShortCut-198            [4, 32, 28, 28]               0
CifNetBasicLayer-199            [4, 32, 28, 28]               0
          Conv2d-200            [4, 32, 28, 28]           9,216
     BatchNorm2d-201            [4, 32, 28, 28]              64
            SiLU-202            [4, 32, 28, 28]               0
 CifNetConvLayer-203            [4, 32, 28, 28]               0
          Conv2d-204            [4, 32, 28, 28]           9,216
     BatchNorm2d-205            [4, 32, 28, 28]              64
            SiLU-206            [4, 32, 28, 28]               0
 CifNetConvLayer-207            [4, 32, 28, 28]               0
        Identity-208            [4, 32, 28, 28]               0
CifNetBasicLayer-209            [4, 32, 28, 28]               0
          Conv2d-210            [4, 32, 28, 28]           9,216
     BatchNorm2d-211            [4, 32, 28, 28]              64
            SiLU-212            [4, 32, 28, 28]               0
 CifNetConvLayer-213            [4, 32, 28, 28]               0
          Conv2d-214            [4, 32, 28, 28]           9,216
     BatchNorm2d-215            [4, 32, 28, 28]              64
            SiLU-216            [4, 32, 28, 28]               0
 CifNetConvLayer-217            [4, 32, 28, 28]               0
        Identity-218            [4, 32, 28, 28]               0
CifNetBasicLayer-219            [4, 32, 28, 28]               0
          Conv2d-220            [4, 32, 28, 28]           9,216
     BatchNorm2d-221            [4, 32, 28, 28]              64
            SiLU-222            [4, 32, 28, 28]               0
 CifNetConvLayer-223            [4, 32, 28, 28]               0
          Conv2d-224            [4, 32, 28, 28]           9,216
     BatchNorm2d-225            [4, 32, 28, 28]              64
            SiLU-226            [4, 32, 28, 28]               0
 CifNetConvLayer-227            [4, 32, 28, 28]               0
        Identity-228            [4, 32, 28, 28]               0
CifNetBasicLayer-229            [4, 32, 28, 28]               0
          Conv2d-230            [4, 32, 28, 28]           9,216
     BatchNorm2d-231            [4, 32, 28, 28]              64
            SiLU-232            [4, 32, 28, 28]               0
 CifNetConvLayer-233            [4, 32, 28, 28]               0
          Conv2d-234            [4, 32, 28, 28]           9,216
     BatchNorm2d-235            [4, 32, 28, 28]              64
            SiLU-236            [4, 32, 28, 28]               0
 CifNetConvLayer-237            [4, 32, 28, 28]               0
        Identity-238            [4, 32, 28, 28]               0
CifNetBasicLayer-239            [4, 32, 28, 28]               0
          Conv2d-240            [4, 32, 28, 28]           9,216
     BatchNorm2d-241            [4, 32, 28, 28]              64
            SiLU-242            [4, 32, 28, 28]               0
 CifNetConvLayer-243            [4, 32, 28, 28]               0
          Conv2d-244            [4, 32, 28, 28]           9,216
     BatchNorm2d-245            [4, 32, 28, 28]              64
            SiLU-246            [4, 32, 28, 28]               0
 CifNetConvLayer-247            [4, 32, 28, 28]               0
        Identity-248            [4, 32, 28, 28]               0
CifNetBasicLayer-249            [4, 32, 28, 28]               0
          Conv2d-250            [4, 32, 28, 28]           9,216
     BatchNorm2d-251            [4, 32, 28, 28]              64
            SiLU-252            [4, 32, 28, 28]               0
 CifNetConvLayer-253            [4, 32, 28, 28]               0
          Conv2d-254            [4, 32, 28, 28]           9,216
     BatchNorm2d-255            [4, 32, 28, 28]              64
            SiLU-256            [4, 32, 28, 28]               0
 CifNetConvLayer-257            [4, 32, 28, 28]               0
        Identity-258            [4, 32, 28, 28]               0
CifNetBasicLayer-259            [4, 32, 28, 28]               0
          Conv2d-260            [4, 32, 28, 28]           9,216
     BatchNorm2d-261            [4, 32, 28, 28]              64
            SiLU-262            [4, 32, 28, 28]               0
 CifNetConvLayer-263            [4, 32, 28, 28]               0
          Conv2d-264            [4, 32, 28, 28]           9,216
     BatchNorm2d-265            [4, 32, 28, 28]              64
            SiLU-266            [4, 32, 28, 28]               0
 CifNetConvLayer-267            [4, 32, 28, 28]               0
        Identity-268            [4, 32, 28, 28]               0
CifNetBasicLayer-269            [4, 32, 28, 28]               0
          Conv2d-270            [4, 32, 28, 28]           9,216
     BatchNorm2d-271            [4, 32, 28, 28]              64
            SiLU-272            [4, 32, 28, 28]               0
 CifNetConvLayer-273            [4, 32, 28, 28]               0
          Conv2d-274            [4, 32, 28, 28]           9,216
     BatchNorm2d-275            [4, 32, 28, 28]              64
            SiLU-276            [4, 32, 28, 28]               0
 CifNetConvLayer-277            [4, 32, 28, 28]               0
        Identity-278            [4, 32, 28, 28]               0
CifNetBasicLayer-279            [4, 32, 28, 28]               0
          Conv2d-280            [4, 32, 28, 28]           9,216
     BatchNorm2d-281            [4, 32, 28, 28]              64
            SiLU-282            [4, 32, 28, 28]               0
 CifNetConvLayer-283            [4, 32, 28, 28]               0
          Conv2d-284            [4, 32, 28, 28]           9,216
     BatchNorm2d-285            [4, 32, 28, 28]              64
            SiLU-286            [4, 32, 28, 28]               0
 CifNetConvLayer-287            [4, 32, 28, 28]               0
        Identity-288            [4, 32, 28, 28]               0
CifNetBasicLayer-289            [4, 32, 28, 28]               0
          Conv2d-290            [4, 32, 28, 28]           9,216
     BatchNorm2d-291            [4, 32, 28, 28]              64
            SiLU-292            [4, 32, 28, 28]               0
 CifNetConvLayer-293            [4, 32, 28, 28]               0
          Conv2d-294            [4, 32, 28, 28]           9,216
     BatchNorm2d-295            [4, 32, 28, 28]              64
            SiLU-296            [4, 32, 28, 28]               0
 CifNetConvLayer-297            [4, 32, 28, 28]               0
        Identity-298            [4, 32, 28, 28]               0
CifNetBasicLayer-299            [4, 32, 28, 28]               0
          Conv2d-300            [4, 32, 28, 28]           9,216
     BatchNorm2d-301            [4, 32, 28, 28]              64
            SiLU-302            [4, 32, 28, 28]               0
 CifNetConvLayer-303            [4, 32, 28, 28]               0
          Conv2d-304            [4, 32, 28, 28]           9,216
     BatchNorm2d-305            [4, 32, 28, 28]              64
            SiLU-306            [4, 32, 28, 28]               0
 CifNetConvLayer-307            [4, 32, 28, 28]               0
        Identity-308            [4, 32, 28, 28]               0
CifNetBasicLayer-309            [4, 32, 28, 28]               0
          Conv2d-310            [4, 32, 28, 28]           9,216
     BatchNorm2d-311            [4, 32, 28, 28]              64
            SiLU-312            [4, 32, 28, 28]               0
 CifNetConvLayer-313            [4, 32, 28, 28]               0
          Conv2d-314            [4, 32, 28, 28]           9,216
     BatchNorm2d-315            [4, 32, 28, 28]              64
            SiLU-316            [4, 32, 28, 28]               0
 CifNetConvLayer-317            [4, 32, 28, 28]               0
        Identity-318            [4, 32, 28, 28]               0
CifNetBasicLayer-319            [4, 32, 28, 28]               0
          Conv2d-320            [4, 32, 28, 28]           9,216
     BatchNorm2d-321            [4, 32, 28, 28]              64
            SiLU-322            [4, 32, 28, 28]               0
 CifNetConvLayer-323            [4, 32, 28, 28]               0
          Conv2d-324            [4, 32, 28, 28]           9,216
     BatchNorm2d-325            [4, 32, 28, 28]              64
            SiLU-326            [4, 32, 28, 28]               0
 CifNetConvLayer-327            [4, 32, 28, 28]               0
        Identity-328            [4, 32, 28, 28]               0
CifNetBasicLayer-329            [4, 32, 28, 28]               0
          Conv2d-330            [4, 32, 28, 28]           9,216
     BatchNorm2d-331            [4, 32, 28, 28]              64
            SiLU-332            [4, 32, 28, 28]               0
 CifNetConvLayer-333            [4, 32, 28, 28]               0
          Conv2d-334            [4, 32, 28, 28]           9,216
     BatchNorm2d-335            [4, 32, 28, 28]              64
            SiLU-336            [4, 32, 28, 28]               0
 CifNetConvLayer-337            [4, 32, 28, 28]               0
        Identity-338            [4, 32, 28, 28]               0
CifNetBasicLayer-339            [4, 32, 28, 28]               0
          Conv2d-340            [4, 32, 28, 28]           9,216
     BatchNorm2d-341            [4, 32, 28, 28]              64
            SiLU-342            [4, 32, 28, 28]               0
 CifNetConvLayer-343            [4, 32, 28, 28]               0
          Conv2d-344            [4, 32, 28, 28]           9,216
     BatchNorm2d-345            [4, 32, 28, 28]              64
            SiLU-346            [4, 32, 28, 28]               0
 CifNetConvLayer-347            [4, 32, 28, 28]               0
        Identity-348            [4, 32, 28, 28]               0
CifNetBasicLayer-349            [4, 32, 28, 28]               0
          Conv2d-350            [4, 32, 28, 28]           9,216
     BatchNorm2d-351            [4, 32, 28, 28]              64
            SiLU-352            [4, 32, 28, 28]               0
 CifNetConvLayer-353            [4, 32, 28, 28]               0
          Conv2d-354            [4, 32, 28, 28]           9,216
     BatchNorm2d-355            [4, 32, 28, 28]              64
            SiLU-356            [4, 32, 28, 28]               0
 CifNetConvLayer-357            [4, 32, 28, 28]               0
        Identity-358            [4, 32, 28, 28]               0
CifNetBasicLayer-359            [4, 32, 28, 28]               0
          Conv2d-360            [4, 32, 28, 28]           9,216
     BatchNorm2d-361            [4, 32, 28, 28]              64
            SiLU-362            [4, 32, 28, 28]               0
 CifNetConvLayer-363            [4, 32, 28, 28]               0
          Conv2d-364            [4, 32, 28, 28]           9,216
     BatchNorm2d-365            [4, 32, 28, 28]              64
            SiLU-366            [4, 32, 28, 28]               0
 CifNetConvLayer-367            [4, 32, 28, 28]               0
        Identity-368            [4, 32, 28, 28]               0
CifNetBasicLayer-369            [4, 32, 28, 28]               0
     CifNetStage-370            [4, 32, 28, 28]               0
          Conv2d-371            [4, 64, 14, 14]          18,432
     BatchNorm2d-372            [4, 64, 14, 14]             128
            SiLU-373            [4, 64, 14, 14]               0
 CifNetConvLayer-374            [4, 64, 14, 14]               0
          Conv2d-375            [4, 64, 14, 14]          36,864
     BatchNorm2d-376            [4, 64, 14, 14]             128
            SiLU-377            [4, 64, 14, 14]               0
 CifNetConvLayer-378            [4, 64, 14, 14]               0
          Conv2d-379            [4, 64, 14, 14]           2,048
     BatchNorm2d-380            [4, 64, 14, 14]             128
  CifNetShortCut-381            [4, 64, 14, 14]               0
CifNetBasicLayer-382            [4, 64, 14, 14]               0
          Conv2d-383            [4, 64, 14, 14]          36,864
     BatchNorm2d-384            [4, 64, 14, 14]             128
            SiLU-385            [4, 64, 14, 14]               0
 CifNetConvLayer-386            [4, 64, 14, 14]               0
          Conv2d-387            [4, 64, 14, 14]          36,864
     BatchNorm2d-388            [4, 64, 14, 14]             128
            SiLU-389            [4, 64, 14, 14]               0
 CifNetConvLayer-390            [4, 64, 14, 14]               0
        Identity-391            [4, 64, 14, 14]               0
CifNetBasicLayer-392            [4, 64, 14, 14]               0
          Conv2d-393            [4, 64, 14, 14]          36,864
     BatchNorm2d-394            [4, 64, 14, 14]             128
            SiLU-395            [4, 64, 14, 14]               0
 CifNetConvLayer-396            [4, 64, 14, 14]               0
          Conv2d-397            [4, 64, 14, 14]          36,864
     BatchNorm2d-398            [4, 64, 14, 14]             128
            SiLU-399            [4, 64, 14, 14]               0
 CifNetConvLayer-400            [4, 64, 14, 14]               0
        Identity-401            [4, 64, 14, 14]               0
CifNetBasicLayer-402            [4, 64, 14, 14]               0
          Conv2d-403            [4, 64, 14, 14]          36,864
     BatchNorm2d-404            [4, 64, 14, 14]             128
            SiLU-405            [4, 64, 14, 14]               0
 CifNetConvLayer-406            [4, 64, 14, 14]               0
          Conv2d-407            [4, 64, 14, 14]          36,864
     BatchNorm2d-408            [4, 64, 14, 14]             128
            SiLU-409            [4, 64, 14, 14]               0
 CifNetConvLayer-410            [4, 64, 14, 14]               0
        Identity-411            [4, 64, 14, 14]               0
CifNetBasicLayer-412            [4, 64, 14, 14]               0
          Conv2d-413            [4, 64, 14, 14]          36,864
     BatchNorm2d-414            [4, 64, 14, 14]             128
            SiLU-415            [4, 64, 14, 14]               0
 CifNetConvLayer-416            [4, 64, 14, 14]               0
          Conv2d-417            [4, 64, 14, 14]          36,864
     BatchNorm2d-418            [4, 64, 14, 14]             128
            SiLU-419            [4, 64, 14, 14]               0
 CifNetConvLayer-420            [4, 64, 14, 14]               0
        Identity-421            [4, 64, 14, 14]               0
CifNetBasicLayer-422            [4, 64, 14, 14]               0
          Conv2d-423            [4, 64, 14, 14]          36,864
     BatchNorm2d-424            [4, 64, 14, 14]             128
            SiLU-425            [4, 64, 14, 14]               0
 CifNetConvLayer-426            [4, 64, 14, 14]               0
          Conv2d-427            [4, 64, 14, 14]          36,864
     BatchNorm2d-428            [4, 64, 14, 14]             128
            SiLU-429            [4, 64, 14, 14]               0
 CifNetConvLayer-430            [4, 64, 14, 14]               0
        Identity-431            [4, 64, 14, 14]               0
CifNetBasicLayer-432            [4, 64, 14, 14]               0
          Conv2d-433            [4, 64, 14, 14]          36,864
     BatchNorm2d-434            [4, 64, 14, 14]             128
            SiLU-435            [4, 64, 14, 14]               0
 CifNetConvLayer-436            [4, 64, 14, 14]               0
          Conv2d-437            [4, 64, 14, 14]          36,864
     BatchNorm2d-438            [4, 64, 14, 14]             128
            SiLU-439            [4, 64, 14, 14]               0
 CifNetConvLayer-440            [4, 64, 14, 14]               0
        Identity-441            [4, 64, 14, 14]               0
CifNetBasicLayer-442            [4, 64, 14, 14]               0
          Conv2d-443            [4, 64, 14, 14]          36,864
     BatchNorm2d-444            [4, 64, 14, 14]             128
            SiLU-445            [4, 64, 14, 14]               0
 CifNetConvLayer-446            [4, 64, 14, 14]               0
          Conv2d-447            [4, 64, 14, 14]          36,864
     BatchNorm2d-448            [4, 64, 14, 14]             128
            SiLU-449            [4, 64, 14, 14]               0
 CifNetConvLayer-450            [4, 64, 14, 14]               0
        Identity-451            [4, 64, 14, 14]               0
CifNetBasicLayer-452            [4, 64, 14, 14]               0
          Conv2d-453            [4, 64, 14, 14]          36,864
     BatchNorm2d-454            [4, 64, 14, 14]             128
            SiLU-455            [4, 64, 14, 14]               0
 CifNetConvLayer-456            [4, 64, 14, 14]               0
          Conv2d-457            [4, 64, 14, 14]          36,864
     BatchNorm2d-458            [4, 64, 14, 14]             128
            SiLU-459            [4, 64, 14, 14]               0
 CifNetConvLayer-460            [4, 64, 14, 14]               0
        Identity-461            [4, 64, 14, 14]               0
CifNetBasicLayer-462            [4, 64, 14, 14]               0
          Conv2d-463            [4, 64, 14, 14]          36,864
     BatchNorm2d-464            [4, 64, 14, 14]             128
            SiLU-465            [4, 64, 14, 14]               0
 CifNetConvLayer-466            [4, 64, 14, 14]               0
          Conv2d-467            [4, 64, 14, 14]          36,864
     BatchNorm2d-468            [4, 64, 14, 14]             128
            SiLU-469            [4, 64, 14, 14]               0
 CifNetConvLayer-470            [4, 64, 14, 14]               0
        Identity-471            [4, 64, 14, 14]               0
CifNetBasicLayer-472            [4, 64, 14, 14]               0
          Conv2d-473            [4, 64, 14, 14]          36,864
     BatchNorm2d-474            [4, 64, 14, 14]             128
            SiLU-475            [4, 64, 14, 14]               0
 CifNetConvLayer-476            [4, 64, 14, 14]               0
          Conv2d-477            [4, 64, 14, 14]          36,864
     BatchNorm2d-478            [4, 64, 14, 14]             128
            SiLU-479            [4, 64, 14, 14]               0
 CifNetConvLayer-480            [4, 64, 14, 14]               0
        Identity-481            [4, 64, 14, 14]               0
CifNetBasicLayer-482            [4, 64, 14, 14]               0
          Conv2d-483            [4, 64, 14, 14]          36,864
     BatchNorm2d-484            [4, 64, 14, 14]             128
            SiLU-485            [4, 64, 14, 14]               0
 CifNetConvLayer-486            [4, 64, 14, 14]               0
          Conv2d-487            [4, 64, 14, 14]          36,864
     BatchNorm2d-488            [4, 64, 14, 14]             128
            SiLU-489            [4, 64, 14, 14]               0
 CifNetConvLayer-490            [4, 64, 14, 14]               0
        Identity-491            [4, 64, 14, 14]               0
CifNetBasicLayer-492            [4, 64, 14, 14]               0
          Conv2d-493            [4, 64, 14, 14]          36,864
     BatchNorm2d-494            [4, 64, 14, 14]             128
            SiLU-495            [4, 64, 14, 14]               0
 CifNetConvLayer-496            [4, 64, 14, 14]               0
          Conv2d-497            [4, 64, 14, 14]          36,864
     BatchNorm2d-498            [4, 64, 14, 14]             128
            SiLU-499            [4, 64, 14, 14]               0
 CifNetConvLayer-500            [4, 64, 14, 14]               0
        Identity-501            [4, 64, 14, 14]               0
CifNetBasicLayer-502            [4, 64, 14, 14]               0
          Conv2d-503            [4, 64, 14, 14]          36,864
     BatchNorm2d-504            [4, 64, 14, 14]             128
            SiLU-505            [4, 64, 14, 14]               0
 CifNetConvLayer-506            [4, 64, 14, 14]               0
          Conv2d-507            [4, 64, 14, 14]          36,864
     BatchNorm2d-508            [4, 64, 14, 14]             128
            SiLU-509            [4, 64, 14, 14]               0
 CifNetConvLayer-510            [4, 64, 14, 14]               0
        Identity-511            [4, 64, 14, 14]               0
CifNetBasicLayer-512            [4, 64, 14, 14]               0
          Conv2d-513            [4, 64, 14, 14]          36,864
     BatchNorm2d-514            [4, 64, 14, 14]             128
            SiLU-515            [4, 64, 14, 14]               0
 CifNetConvLayer-516            [4, 64, 14, 14]               0
          Conv2d-517            [4, 64, 14, 14]          36,864
     BatchNorm2d-518            [4, 64, 14, 14]             128
            SiLU-519            [4, 64, 14, 14]               0
 CifNetConvLayer-520            [4, 64, 14, 14]               0
        Identity-521            [4, 64, 14, 14]               0
CifNetBasicLayer-522            [4, 64, 14, 14]               0
          Conv2d-523            [4, 64, 14, 14]          36,864
     BatchNorm2d-524            [4, 64, 14, 14]             128
            SiLU-525            [4, 64, 14, 14]               0
 CifNetConvLayer-526            [4, 64, 14, 14]               0
          Conv2d-527            [4, 64, 14, 14]          36,864
     BatchNorm2d-528            [4, 64, 14, 14]             128
            SiLU-529            [4, 64, 14, 14]               0
 CifNetConvLayer-530            [4, 64, 14, 14]               0
        Identity-531            [4, 64, 14, 14]               0
CifNetBasicLayer-532            [4, 64, 14, 14]               0
          Conv2d-533            [4, 64, 14, 14]          36,864
     BatchNorm2d-534            [4, 64, 14, 14]             128
            SiLU-535            [4, 64, 14, 14]               0
 CifNetConvLayer-536            [4, 64, 14, 14]               0
          Conv2d-537            [4, 64, 14, 14]          36,864
     BatchNorm2d-538            [4, 64, 14, 14]             128
            SiLU-539            [4, 64, 14, 14]               0
 CifNetConvLayer-540            [4, 64, 14, 14]               0
        Identity-541            [4, 64, 14, 14]               0
CifNetBasicLayer-542            [4, 64, 14, 14]               0
          Conv2d-543            [4, 64, 14, 14]          36,864
     BatchNorm2d-544            [4, 64, 14, 14]             128
            SiLU-545            [4, 64, 14, 14]               0
 CifNetConvLayer-546            [4, 64, 14, 14]               0
          Conv2d-547            [4, 64, 14, 14]          36,864
     BatchNorm2d-548            [4, 64, 14, 14]             128
            SiLU-549            [4, 64, 14, 14]               0
 CifNetConvLayer-550            [4, 64, 14, 14]               0
        Identity-551            [4, 64, 14, 14]               0
CifNetBasicLayer-552            [4, 64, 14, 14]               0
     CifNetStage-553            [4, 64, 14, 14]               0
   CifNetEncoder-554         [[-1, 64, 14, 14]]               0
AdaptiveAvgPool2d-555              [4, 64, 1, 1]               0
     CifNetModel-556  [[-1, 64, 14, 14], [-1, 64, 1, 1]]               0
         Flatten-557                    [4, 64]               0
          Linear-558                    [4, 10]             650
================================================================
Total params: 1,732,634
Trainable params: 1,732,634
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 2.30
Forward/backward pass size (MB): 520.92
Params size (MB): 6.61
Estimated Total Size (MB): 529.82
----------------------------------------------------------------