code: def __init__(
      self,
      blocks,
      padding_list,
      dilation_list,
      channels,
      kernel_size,
      num_branches,
  ):
      super(AFN, self).__init__()
  
      # parameters in the architecture
      self.channels = channels
      self.kernel_size = kernel_size
      self.padding_list = padding_list
      self.dilation_list = dilation_list
      self.num_branches = num_branches
      self.blocks = blocks
  
      # network architecture
      self.conv1 = nn.Conv3d(
          self.channels[0],
          self.channels[1],
          kernel_size=self.kernel_size,
      )
      self.bn1 = nn.BatchNorm3d(self.channels[1])
      self.relu = nn.ReLU(inplace=True)
      self.conv2 = nn.Conv3d(
          self.channels[1],
          self.channels[2],
          kernel_size=self.kernel_size,
      )
      self.bn2 = nn.BatchNorm3d(self.channels[2])
  
      self.layers = nn.ModuleList()
      for i in range(len(blocks)):
          block = blocks[i]
          index = int(2 * i + 2)
          if block == BasicBlock:
              self.layers.append(
                  block(
                      self.channels[index],
                      self.channels[index + 1],
                      self.channels[index + 2],
                  )
              )
          else:
              self.layers.append(
                  block(
                      self.channels[index],
                      self.channels[index + 1],
                      self.channels[index + 2],
                      self.padding_list,
                      self.dilation_list,
                      self.num_branches,
                  )
              )
  
      self.fc = nn.Conv3d(
          self.channels[8], self.channels[9], kernel_size=1
      )
  
      for m in self.modules():
          if isinstance(m, nn.Conv3d):
              m.weight.data.normal_(0, 0.01)
          elif isinstance(m, nn.BatchNorm3d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new AFN object from inherited base classes: nn with example: obj = AFN(blocks, padding_list, dilation_list, channels, kernel_size, num_branches)