code: def forward(self, conv_out, segSize=None):
      conv5 = conv_out[-1]
  
      x = self.cbr(conv5)
      x = self.conv_last(x)
  
      if self.use_softmax:  # is True during inference
          x = nn.functional.upsample(x, size=segSize, mode="bilinear")
          x = nn.functional.softmax(x, dim=1)
          return x
  
      # deep sup
      conv4 = conv_out[-2]
      _ = self.cbr_deepsup(conv4)
      _ = self.conv_last_deepsup(_)
  
      x = nn.functional.log_softmax(x, dim=1)
      _ = nn.functional.log_softmax(_, dim=1)
  
      return (x, _)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .None import seg_resnet
  from .None import seg_resnext
label_desc: Use image node_id 2 for calling the C1BilinearDeepSup obj's underlying member method code with example usage: obj.forward(conv_out, segSize) and returns: x,  _, x