code: def _make_one_branch(
      self, branch_index, block_type, num_blocks, num_channels, stride=1
  ):
      downsample = None
      if (
          stride != 1
          or self.num_in_chs[branch_index]
          != num_channels[branch_index] * block_type.expansion
      ):
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.num_in_chs[branch_index],
                  num_channels[branch_index] * block_type.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(
                  num_channels[branch_index] * block_type.expansion,
                  momentum=_BN_MOMENTUM,
              ),
          )
  
      layers = [
          block_type(
              self.num_in_chs[branch_index],
              num_channels[branch_index],
              stride,
              downsample,
          )
      ]
      self.num_in_chs[branch_index] = (
          num_channels[branch_index] * block_type.expansion
      )
      for i in range(1, num_blocks[branch_index]):
          layers.append(
              block_type(
                  self.num_in_chs[branch_index],
                  num_channels[branch_index],
              )
          )
  
      return nn.Sequential(*layers)
  
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 3 for calling the HighResolutionModule obj's underlying member method code with example usage: obj._make_one_branch(branch_index, block_type, num_blocks, num_channels, stride) and returns: nn