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"]
docstr: Forward function.
  Args:
      x (Tensor): Input feature map.
  
  Returns:
      Tensor: Output tensor after Maxpooling layer.
docstr_tok: ["Forward","function",".","Args",":","x","(","Tensor",")",":","Input","feature","map",".","Returns",":","Tensor",":","Output","tensor","after","Maxpooling","layer","."]
code: def forward(self, x) -> torch.Tensor:
      """Forward function.
      Args:
          x (Tensor): Input feature map.
  
      Returns:
          Tensor: Output tensor after Maxpooling layer.
      """
      return self.model(x)
  
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 2 for calling the Maxpool2d obj's underlying member method code with example usage: obj.forward(x) and returns: self