class_docstr: Original implementation: https://github.com/princeton-vl/pytorch_stacked_hourglass/blob/master/models/layers.py
code: def forward(self, x: Tensor) -> List[Tensor]:  # skipcq: PYL-W0221
      x = self.stem(x)
      outputs = [x]
  
      for i, hourglass in enumerate(self.blocks):
          features = self.features[i](hourglass(x))
          outputs.append(features)
  
          if i < self.num_blocks - 1:
              x = x + self.merge_features[i](features)
  
      return outputs
  
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 3 for calling the StackedHGEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: outputs