code: def forward(self, x):
      residual = x[:, :, 4:-4, 4:-4, 4:-4]
      # compute attention weights in the first autofocus convolutional layer
      feature = x.detach()
      att = self.relu(self.convatt11(feature))
      att = self.convatt12(att)
      att = F.softmax(att, dim=1)
      att = att[:, :, 1:-1, 1:-1, 1:-1]
  
      # linear combination of different rates
      x1 = self.conv1(x)
      shape = x1.size()
      x1 = self.bn_list1[0](x1) * att[:, 0:1, :, :, :].expand(shape)
  
      for i in range(1, self.num_branches):
          x2 = F.conv3d(
              x,
              self.conv1.weight,
              padding=self.padding_list[i],
              dilation=self.dilation_list[i],
          )
          x2 = self.bn_list1[i](x2)
          x1 += x2 * att[:, i : (i + 1), :, :, :].expand(shape)
  
      x = self.relu(x1)
  
      # compute attention weights for the second autofocus layer
      feature2 = x.detach()
      att2 = self.relu(self.convatt21(feature2))
      att2 = self.convatt22(att2)
      att2 = F.softmax(att2, dim=1)
      att2 = att2[:, :, 1:-1, 1:-1, 1:-1]
  
      # linear combination of different rates
      x21 = self.conv2(x)
      shape = x21.size()
      x21 = self.bn_list2[0](x21) * att2[:, 0:1, :, :, :].expand(shape)
  
      for i in range(1, self.num_branches):
          x22 = F.conv3d(
              x,
              self.conv2.weight,
              padding=self.padding_list[i],
              dilation=self.dilation_list[i],
          )
          x22 = self.bn_list2[i](x22)
          x21 += x22 * att2[:, i : (i + 1), :, :, :].expand(shape)
  
      if self.downsample is not None:
          residual = self.downsample(residual)
  
      x = x21 + residual
      x = self.relu(x)
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Autofocus obj's underlying member method code with example usage: obj.forward(x) and returns: x