code: def __init__(self, features: int, activation: Callable, blocks=1):
      super().__init__()
      residual_blocks = [
          HGResidualBlock(features, features, activation=activation)
          for _ in range(blocks)
      ]
      self.residuals = nn.Sequential(*residual_blocks)
      self.linear = conv1x1_bn_act(
          features, features, activation=activation
      )
  
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 1 to create a new HGFeaturesBlock object from inherited base classes: nn with example: obj = HGFeaturesBlock(features, activation, blocks)