根据如下代码画出网络结构图：import torchimport torchnn as nnclass ResidualConvnnModule def __init__self input_dim output_dim stride padding superResidualConv self__init__ selfconv_block = nnSequent

网络结构图如下：

ResUnet(
  (input_layer): Sequential(
    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
  (input_skip): Sequential(
    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
  (residual_conv_1): ResidualConv(
    (conv_block): Sequential(
      (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU()
      (2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU()
      (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conv_skip): Sequential(
      (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (residual_conv_2): ResidualConv(
    (conv_block): Sequential(
      (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU()
      (2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU()
      (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conv_skip): Sequential(
      (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (bridge): ResidualConv(
    (conv_block): Sequential(
      (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU()
      (2): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU()
      (5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conv_skip): Sequential(
      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (upsample_1): Upsample(
    (upsample): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2))
  )
  (up_residual_conv1): ResidualConv(
    (conv_block): Sequential(
      (0): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU()
      (2): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU()
      (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conv_skip): Sequential(
      (0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (upsample_2): Upsample(
    (upsample): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
  )
  (up_residual_conv2): ResidualConv(
    (conv_block): Sequential(
      (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU()
      (2): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU()
      (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conv_skip): Sequential(
      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (upsample_3): Upsample(
    (upsample): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))
  )
  (up_residual_conv3): ResidualConv(
    (conv_block): Sequential(
      (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU()
      (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU()
      (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conv_skip): Sequential(
      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (output_layer): Sequential(
    (0): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))
    (1): Sigmoid()
  )
)
``