code: def forward(self, x):
      out = self.firstconv(x)
  
      skip_connections = []
      for i in range(len(self.down_blocks)):
          out = self.denseBlocksDown[i](out)
          skip_connections.append(out)
          out = self.transDownBlocks[i](out)
  
      out = self.bottleneck(out)
      for i in range(len(self.up_blocks)):
          skip = skip_connections.pop()
          out = self.transUpBlocks[i](out, skip)
          out = self.denseBlocksUp[i](out)
  
      out = self.finalConv(out)
      out = self.softmax(out)
      return out
  
imports: import torch
  import torch.functional
  import torch.nn
label_desc: Use image node_id 2 for calling the FCDenseNet obj's underlying member method code with example usage: obj.forward(x) and returns: out