class_docstr: A wrapper around nn.Maxpool2d().
  
  Args:
      kernel_size (int or tuple(int)): Kernel size for max pooling layer
      stride (int or tuple(int)): Stride for max pooling layer
      padding (int or tuple(int)): Padding for pooling layer
class_docstr_tok: ["A","wrapper","around","nn.Maxpool2d","(",")",".","Args",":","kernel_size","(","int","or","tuple","(","int",")",")",":","Kernel","size","for","max","pooling","layer","stride","(","int","or","tuple","(","int",")",")",":","Stride","for","max","pooling","layer","padding","(","int","or","tuple","(","int",")",")",":","Padding","for","pooling","layer"]
code: def __init__(
      self,
      kernel_size: Union[int, Tuple[int]],
      stride: Union[int, Tuple[int]],
      padding: Union[int, Tuple[int]] = 0,
      **kwargs,
  ) -> None:
      super().__init__()
      self.model = nn.MaxPool2d(kernel_size, stride, padding)
  
file_path: mmocr/mmocr/models/textrecog/plugins/common.py
imports: from typing import Tuple, Union
  import torch
  import torch.nn
  from mmocr.registry import MODELS
label_desc: Use image node_id 1 to create a new Maxpool2d object from inherited base classes: nn with example: obj = Maxpool2d(kernel_size, stride, padding)