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): 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()
                )
              )
            )
          )
        )
        (1): 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()
                )
              )
            )
          )
        )
        (2): CifNetStage(
          (layers): Sequential(
            (0): CifNetBasicLayer(
              (shortcut): CifNetShortCut(
                (convolution): Conv2d(64, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
                (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(64, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (1): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (2): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
              )
            )
            (3): CifNetBasicLayer(
              (shortcut): Identity()
              (layer): Sequential(
                (0): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (activation): SiLU()
                )
                (1): CifNetConvLayer(
                  (convolution): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                  (normalization): BatchNorm2d(256, 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=256, 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, 32, 28, 28]           4,608
       BatchNorm2d-8            [4, 32, 28, 28]              64
              SiLU-9            [4, 32, 28, 28]               0
  CifNetConvLayer-10            [4, 32, 28, 28]               0
           Conv2d-11            [4, 32, 28, 28]           9,216
      BatchNorm2d-12            [4, 32, 28, 28]              64
             SiLU-13            [4, 32, 28, 28]               0
  CifNetConvLayer-14            [4, 32, 28, 28]               0
           Conv2d-15            [4, 32, 28, 28]             512
      BatchNorm2d-16            [4, 32, 28, 28]              64
   CifNetShortCut-17            [4, 32, 28, 28]               0
 CifNetBasicLayer-18            [4, 32, 28, 28]               0
           Conv2d-19            [4, 32, 28, 28]           9,216
      BatchNorm2d-20            [4, 32, 28, 28]              64
             SiLU-21            [4, 32, 28, 28]               0
  CifNetConvLayer-22            [4, 32, 28, 28]               0
           Conv2d-23            [4, 32, 28, 28]           9,216
      BatchNorm2d-24            [4, 32, 28, 28]              64
             SiLU-25            [4, 32, 28, 28]               0
  CifNetConvLayer-26            [4, 32, 28, 28]               0
         Identity-27            [4, 32, 28, 28]               0
 CifNetBasicLayer-28            [4, 32, 28, 28]               0
           Conv2d-29            [4, 32, 28, 28]           9,216
      BatchNorm2d-30            [4, 32, 28, 28]              64
             SiLU-31            [4, 32, 28, 28]               0
  CifNetConvLayer-32            [4, 32, 28, 28]               0
           Conv2d-33            [4, 32, 28, 28]           9,216
      BatchNorm2d-34            [4, 32, 28, 28]              64
             SiLU-35            [4, 32, 28, 28]               0
  CifNetConvLayer-36            [4, 32, 28, 28]               0
         Identity-37            [4, 32, 28, 28]               0
 CifNetBasicLayer-38            [4, 32, 28, 28]               0
           Conv2d-39            [4, 32, 28, 28]           9,216
      BatchNorm2d-40            [4, 32, 28, 28]              64
             SiLU-41            [4, 32, 28, 28]               0
  CifNetConvLayer-42            [4, 32, 28, 28]               0
           Conv2d-43            [4, 32, 28, 28]           9,216
      BatchNorm2d-44            [4, 32, 28, 28]              64
             SiLU-45            [4, 32, 28, 28]               0
  CifNetConvLayer-46            [4, 32, 28, 28]               0
         Identity-47            [4, 32, 28, 28]               0
 CifNetBasicLayer-48            [4, 32, 28, 28]               0
      CifNetStage-49            [4, 32, 28, 28]               0
           Conv2d-50            [4, 64, 14, 14]          18,432
      BatchNorm2d-51            [4, 64, 14, 14]             128
             SiLU-52            [4, 64, 14, 14]               0
  CifNetConvLayer-53            [4, 64, 14, 14]               0
           Conv2d-54            [4, 64, 14, 14]          36,864
      BatchNorm2d-55            [4, 64, 14, 14]             128
             SiLU-56            [4, 64, 14, 14]               0
  CifNetConvLayer-57            [4, 64, 14, 14]               0
           Conv2d-58            [4, 64, 14, 14]           2,048
      BatchNorm2d-59            [4, 64, 14, 14]             128
   CifNetShortCut-60            [4, 64, 14, 14]               0
 CifNetBasicLayer-61            [4, 64, 14, 14]               0
           Conv2d-62            [4, 64, 14, 14]          36,864
      BatchNorm2d-63            [4, 64, 14, 14]             128
             SiLU-64            [4, 64, 14, 14]               0
  CifNetConvLayer-65            [4, 64, 14, 14]               0
           Conv2d-66            [4, 64, 14, 14]          36,864
      BatchNorm2d-67            [4, 64, 14, 14]             128
             SiLU-68            [4, 64, 14, 14]               0
  CifNetConvLayer-69            [4, 64, 14, 14]               0
         Identity-70            [4, 64, 14, 14]               0
 CifNetBasicLayer-71            [4, 64, 14, 14]               0
           Conv2d-72            [4, 64, 14, 14]          36,864
      BatchNorm2d-73            [4, 64, 14, 14]             128
             SiLU-74            [4, 64, 14, 14]               0
  CifNetConvLayer-75            [4, 64, 14, 14]               0
           Conv2d-76            [4, 64, 14, 14]          36,864
      BatchNorm2d-77            [4, 64, 14, 14]             128
             SiLU-78            [4, 64, 14, 14]               0
  CifNetConvLayer-79            [4, 64, 14, 14]               0
         Identity-80            [4, 64, 14, 14]               0
 CifNetBasicLayer-81            [4, 64, 14, 14]               0
           Conv2d-82            [4, 64, 14, 14]          36,864
      BatchNorm2d-83            [4, 64, 14, 14]             128
             SiLU-84            [4, 64, 14, 14]               0
  CifNetConvLayer-85            [4, 64, 14, 14]               0
           Conv2d-86            [4, 64, 14, 14]          36,864
      BatchNorm2d-87            [4, 64, 14, 14]             128
             SiLU-88            [4, 64, 14, 14]               0
  CifNetConvLayer-89            [4, 64, 14, 14]               0
         Identity-90            [4, 64, 14, 14]               0
 CifNetBasicLayer-91            [4, 64, 14, 14]               0
           Conv2d-92            [4, 64, 14, 14]          36,864
      BatchNorm2d-93            [4, 64, 14, 14]             128
             SiLU-94            [4, 64, 14, 14]               0
  CifNetConvLayer-95            [4, 64, 14, 14]               0
           Conv2d-96            [4, 64, 14, 14]          36,864
      BatchNorm2d-97            [4, 64, 14, 14]             128
             SiLU-98            [4, 64, 14, 14]               0
  CifNetConvLayer-99            [4, 64, 14, 14]               0
        Identity-100            [4, 64, 14, 14]               0
CifNetBasicLayer-101            [4, 64, 14, 14]               0
          Conv2d-102            [4, 64, 14, 14]          36,864
     BatchNorm2d-103            [4, 64, 14, 14]             128
            SiLU-104            [4, 64, 14, 14]               0
 CifNetConvLayer-105            [4, 64, 14, 14]               0
          Conv2d-106            [4, 64, 14, 14]          36,864
     BatchNorm2d-107            [4, 64, 14, 14]             128
            SiLU-108            [4, 64, 14, 14]               0
 CifNetConvLayer-109            [4, 64, 14, 14]               0
        Identity-110            [4, 64, 14, 14]               0
CifNetBasicLayer-111            [4, 64, 14, 14]               0
          Conv2d-112            [4, 64, 14, 14]          36,864
     BatchNorm2d-113            [4, 64, 14, 14]             128
            SiLU-114            [4, 64, 14, 14]               0
 CifNetConvLayer-115            [4, 64, 14, 14]               0
          Conv2d-116            [4, 64, 14, 14]          36,864
     BatchNorm2d-117            [4, 64, 14, 14]             128
            SiLU-118            [4, 64, 14, 14]               0
 CifNetConvLayer-119            [4, 64, 14, 14]               0
        Identity-120            [4, 64, 14, 14]               0
CifNetBasicLayer-121            [4, 64, 14, 14]               0
          Conv2d-122            [4, 64, 14, 14]          36,864
     BatchNorm2d-123            [4, 64, 14, 14]             128
            SiLU-124            [4, 64, 14, 14]               0
 CifNetConvLayer-125            [4, 64, 14, 14]               0
          Conv2d-126            [4, 64, 14, 14]          36,864
     BatchNorm2d-127            [4, 64, 14, 14]             128
            SiLU-128            [4, 64, 14, 14]               0
 CifNetConvLayer-129            [4, 64, 14, 14]               0
        Identity-130            [4, 64, 14, 14]               0
CifNetBasicLayer-131            [4, 64, 14, 14]               0
     CifNetStage-132            [4, 64, 14, 14]               0
          Conv2d-133             [4, 256, 7, 7]         147,456
     BatchNorm2d-134             [4, 256, 7, 7]             512
            SiLU-135             [4, 256, 7, 7]               0
 CifNetConvLayer-136             [4, 256, 7, 7]               0
          Conv2d-137             [4, 256, 7, 7]         589,824
     BatchNorm2d-138             [4, 256, 7, 7]             512
            SiLU-139             [4, 256, 7, 7]               0
 CifNetConvLayer-140             [4, 256, 7, 7]               0
          Conv2d-141             [4, 256, 7, 7]          16,384
     BatchNorm2d-142             [4, 256, 7, 7]             512
  CifNetShortCut-143             [4, 256, 7, 7]               0
CifNetBasicLayer-144             [4, 256, 7, 7]               0
          Conv2d-145             [4, 256, 7, 7]         589,824
     BatchNorm2d-146             [4, 256, 7, 7]             512
            SiLU-147             [4, 256, 7, 7]               0
 CifNetConvLayer-148             [4, 256, 7, 7]               0
          Conv2d-149             [4, 256, 7, 7]         589,824
     BatchNorm2d-150             [4, 256, 7, 7]             512
            SiLU-151             [4, 256, 7, 7]               0
 CifNetConvLayer-152             [4, 256, 7, 7]               0
        Identity-153             [4, 256, 7, 7]               0
CifNetBasicLayer-154             [4, 256, 7, 7]               0
          Conv2d-155             [4, 256, 7, 7]         589,824
     BatchNorm2d-156             [4, 256, 7, 7]             512
            SiLU-157             [4, 256, 7, 7]               0
 CifNetConvLayer-158             [4, 256, 7, 7]               0
          Conv2d-159             [4, 256, 7, 7]         589,824
     BatchNorm2d-160             [4, 256, 7, 7]             512
            SiLU-161             [4, 256, 7, 7]               0
 CifNetConvLayer-162             [4, 256, 7, 7]               0
        Identity-163             [4, 256, 7, 7]               0
CifNetBasicLayer-164             [4, 256, 7, 7]               0
          Conv2d-165             [4, 256, 7, 7]         589,824
     BatchNorm2d-166             [4, 256, 7, 7]             512
            SiLU-167             [4, 256, 7, 7]               0
 CifNetConvLayer-168             [4, 256, 7, 7]               0
          Conv2d-169             [4, 256, 7, 7]         589,824
     BatchNorm2d-170             [4, 256, 7, 7]             512
            SiLU-171             [4, 256, 7, 7]               0
 CifNetConvLayer-172             [4, 256, 7, 7]               0
        Identity-173             [4, 256, 7, 7]               0
CifNetBasicLayer-174             [4, 256, 7, 7]               0
     CifNetStage-175             [4, 256, 7, 7]               0
   CifNetEncoder-176          [[-1, 256, 7, 7]]               0
AdaptiveAvgPool2d-177             [4, 256, 1, 1]               0
     CifNetModel-178  [[-1, 256, 7, 7], [-1, 256, 1, 1]]               0
         Flatten-179                   [4, 256]               0
          Linear-180                    [4, 10]           2,570
================================================================
Total params: 4,947,994
Trainable params: 4,947,994
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 2.30
Forward/backward pass size (MB): 133.14
Params size (MB): 18.88
Estimated Total Size (MB): 154.31
----------------------------------------------------------------