code: def __init__(
      self,
      inplanes1,
      outplanes1,
      outplanes2,
      padding_list,
      dilation_list,
      num_branches,
      kernel=3,
  ):
      super(Autofocus, self).__init__()
      self.padding_list = padding_list
      self.dilation_list = dilation_list
      self.num_branches = num_branches
  
      self.conv1 = nn.Conv3d(
          inplanes1,
          outplanes1,
          kernel_size=kernel,
          dilation=self.dilation_list[0],
      )
      self.convatt11 = nn.Conv3d(
          inplanes1, int(inplanes1 / 2), kernel_size=kernel
      )
      self.convatt12 = nn.Conv3d(
          int(inplanes1 / 2), self.num_branches, kernel_size=1
      )
      self.bn_list1 = nn.ModuleList()
      for i in range(self.num_branches):
          self.bn_list1.append(nn.BatchNorm3d(outplanes1))
  
      self.conv2 = nn.Conv3d(
          outplanes1,
          outplanes2,
          kernel_size=kernel,
          dilation=self.dilation_list[0],
      )
      self.convatt21 = nn.Conv3d(
          outplanes1, int(outplanes1 / 2), kernel_size=kernel
      )
      self.convatt22 = nn.Conv3d(
          int(outplanes1 / 2), self.num_branches, kernel_size=1
      )
      self.bn_list2 = nn.ModuleList()
      for i in range(self.num_branches):
          self.bn_list2.append(nn.BatchNorm3d(outplanes2))
  
      self.relu = nn.ReLU(inplace=True)
      if inplanes1 == outplanes2:
          self.downsample = None
      else:
          self.downsample = nn.Sequential(
              nn.Conv3d(inplanes1, outplanes2, kernel_size=1),
              nn.BatchNorm3d(outplanes2),
          )
  
      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 Autofocus object from inherited base classes: nn with example: obj = Autofocus(inplanes1, outplanes1, outplanes2, padding_list, dilation_list, num_branches, kernel)