code: def __init__(
      self, dilation_list, channels, kernel_size, num_branches
  ):
      super(ASPP_s, self).__init__()
  
      # parameters in the architecture
      channels.insert(-1, 120)
      self.channels = channels
      self.kernel_size = kernel_size
      self.padding_list = dilation_list
      self.dilation_list = dilation_list
      self.num_branches = num_branches
  
      # 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.layer3 = BasicBlock(
          self.channels[2], self.channels[3], self.channels[4]
      )
      self.layer4 = BasicBlock(
          self.channels[4], self.channels[5], self.channels[6]
      )
      self.layer5 = BasicBlock(
          self.channels[6], self.channels[7], self.channels[8]
      )
  
      self.conv_list = nn.ModuleList()
      self.bn_list = nn.ModuleList()
      self.last_list = nn.ModuleList()
      for i in range(self.num_branches):
          self.conv_list.append(
              nn.Conv3d(
                  self.channels[8],
                  self.channels[9],
                  kernel_size=self.kernel_size,
                  padding=self.padding_list[i],
                  dilation=self.dilation_list[i],
              )
          )
          self.bn_list.append(nn.BatchNorm3d(self.channels[9]))
          self.last_list.append(
              nn.Conv3d(
                  self.channels[9], self.channels[10], 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 ASPP_s object from inherited base classes: nn with example: obj = ASPP_s(dilation_list, channels, kernel_size, num_branches)