code: def forward(self, inputs, target=None):
      loss = None
  
      # Auxiliary loss
      if self.aux_head is not None:
          loss = self.aux_head(inputs[-2], target)
  
      # Spatial Modulation
      outs = self.spatial_modulation(inputs)
  
      # Temporal Modulation
      outs = [
          temporal_modulation(outs[i])
          for i, temporal_modulation in enumerate(
              self.temporal_modulation_ops
          )
      ]
  
      temporal_modulation_outs = outs
  
      # Build top-down flow - upsampling operation
      if self.upsampling_ops is not None:
          for i in range(self.num_ins - 1, 0, -1):
              outs[i - 1] = outs[i - 1] + self.upsampling_ops[i - 1](
                  outs[i]
              )
  
      # Get top-down outs
      topdownouts = self.level_fusion_op2(outs)
      outs = temporal_modulation_outs
  
      # Build bottom-up flow - downsampling operation
      if self.downsampling_ops is not None:
          for i in range(0, self.num_ins - 1, 1):
              outs[i + 1] = outs[i + 1] + self.downsampling_ops[i](
                  outs[i]
              )
  
      # Get bottom-up outs
      outs = self.level_fusion_op(outs)
  
      # fuse two pyramid outs
      outs = self.pyramid_fusion_op(torch.cat([topdownouts, outs], 1))
  
      return outs, loss
  
imports: import numpy
  import torch
  import torch.nn
  import torch.utils.checkpoint
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the TPN obj's underlying member method code with example usage: obj.forward(inputs, target) and returns: outs,  loss