class_docstr: Args:
      in_channels (int): Number of channels of input image tensor.
      stem_channels (list[int]): List of channels in each stem layer. E.g.,
          [64, 128] stands for 64 and 128 channels in the first and second
          stem layers.
      block_cfgs (dict): Configs of block
      arch_layers (list[int]): List of Block number for each stage.
      arch_channels (list[int]): List of channels for each stage.
      strides (Sequence[int] or Sequence[tuple]): Strides of the first block
          of each stage.
      out_indices (Sequence[int], optional): Indices of output stages. If not
          specified, only the last stage will be returned.
      plugins (dict, optional): Configs of stage plugins
      init_cfg (dict or list[dict], optional): Initialization config dict.
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docstr: Make stem layers.
  
  Args:
      in_channels (int): Number of input channels.
      stem_channels (list[int] or int): List of channels in each stem
          layer. If int, only one stem layer will be created.
docstr_tok: ["Make","stem","layers",".","Args",":","in_channels","(","int",")",":","Number","of","input","channels",".","stem_channels","(","list","[","int","]","or","int",")",":","List","of","channels","in","each","stem","layer",".","If","int",",","only","one","stem","layer","will","be","created","."]
code: def _make_stem_layer(
      self, in_channels: int, stem_channels: Union[int, List[int]]
  ) -> None:
      """Make stem layers.
  
      Args:
          in_channels (int): Number of input channels.
          stem_channels (list[int] or int): List of channels in each stem
              layer. If int, only one stem layer will be created.
      """
      if isinstance(stem_channels, int):
          stem_channels = [stem_channels]
      stem_layers = []
      for _, channels in enumerate(stem_channels):
          stem_layer = ConvModule(
              in_channels,
              channels,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=False,
              norm_cfg=dict(type="BN"),
              act_cfg=dict(type="ReLU"),
          )
          in_channels = channels
          stem_layers.append(stem_layer)
      self.stem_layers = Sequential(*stem_layers)
      self.inplanes = stem_channels[-1]
  
file_path: mmocr/mmocr/models/textrecog/backbones/resnet.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from mmcv.cnn import ConvModule, build_plugin_layer
  from mmengine.model import BaseModule, Sequential
  import mmocr.utils
  from mmocr.models.textrecog.layers import BasicBlock
  from mmocr.registry import MODELS
label_desc: Use image node_id 3 for calling the ResNet obj's underlying member method code with example usage: obj._make_stem_layer(in_channels, stem_channels) without return types