code: def _make_branches(
      self, num_branches, block_type, num_blocks, num_channels
  ):
      branches = []
      for i in range(num_branches):
          branches.append(
              self._make_one_branch(
                  i, block_type, num_blocks, num_channels
              )
          )
  
      return nn.ModuleList(branches)
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._features import FeatureInfo
  from ._registry import register_model, generate_default_cfgs
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 4 for calling the HighResolutionModule obj's underlying member method code with example usage: obj._make_branches(num_branches, block_type, num_blocks, num_channels) and returns: nn