code: def _check_branches(
      self,
      num_branches,
      block_types,
      num_blocks,
      num_in_chs,
      num_channels,
  ):
      error_msg = ""
      if num_branches != len(num_blocks):
          error_msg = "num_branches({}) <> num_blocks({})".format(
              num_branches, len(num_blocks)
          )
      elif num_branches != len(num_channels):
          error_msg = "num_branches({}) <> num_channels({})".format(
              num_branches, len(num_channels)
          )
      elif num_branches != len(num_in_chs):
          error_msg = "num_branches({}) <> num_in_chs({})".format(
              num_branches, len(num_in_chs)
          )
      if error_msg:
          _logger.error(error_msg)
          raise ValueError(error_msg)
  
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 2 for calling the HighResolutionModule obj's underlying member method code with example usage: obj._check_branches(num_branches, block_types, num_blocks, num_in_chs, num_channels) without return types