code: def forward(
      self, x: Tensor
  ) -> Tuple[List[Tensor], List[Tensor]]:  # skipcq: PYL-W0221
      x = self.stem(x)
      outputs = [x]
      supervision = []
  
      for i, hourglass in enumerate(self.blocks):
          features = self.features[i](hourglass(x))
          outputs.append(features)
  
          if i < self.num_blocks - 1:
              sup_mask, sup_features = self.supervision_blocks[i](
                  features
              )
              supervision.append(sup_mask)
              x = x + self.merge_features[i](features) + sup_features
  
      return outputs, supervision
  
imports: import inspect
  from collections import OrderedDict
  from typing import List, Callable, Tuple
  import torch
  from pytorch_toolbelt.modules import ACT_RELU, get_activation_block
  from pytorch_toolbelt.modules.encoders import EncoderModule, make_n_channel_input
  from torch import nn, Tensor
label_desc: Use image node_id 2 for calling the StackedSupervisedHGEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: outputs,  supervision