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.
class_docstr_tok: ["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","."]
docstr: Forward tensor through plugin.
  
  Args:
      x (torch.Tensor): Input tensor.
      plugin_name (list[str]): Name of plugins.
  
  Returns:
      torch.Tensor: Output tensor.
docstr_tok: ["Forward","tensor","through","plugin",".","Args",":","x","(","torch.Tensor",")",":","Input","tensor",".","plugin_name","(","list","[","str","]",")",":","Name","of","plugins",".","Returns",":","torch.Tensor",":","Output","tensor","."]
code: def forward_plugin(
      self, x: torch.Tensor, plugin_name: List[str]
  ) -> torch.Tensor:
      """Forward tensor through plugin.
  
      Args:
          x (torch.Tensor): Input tensor.
          plugin_name (list[str]): Name of plugins.
  
      Returns:
          torch.Tensor: Output tensor.
      """
      out = x
      for name in plugin_name:
          out = getattr(self, name)(out)
      return out
  
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 5 for calling the ResNet obj's underlying member method code with example usage: obj.forward_plugin(x, plugin_name) and returns: out