class_docstr: Applies a 2D average pooling over an input signal composed of several
  input planes.
  
  It can also be used as a network plugin.
  
  Args:
      kernel_size (int or tuple(int)): the size of the window.
      stride (int or tuple(int), optional): the stride of the window.
          Defaults to None.
      padding (int or tuple(int)): implicit zero padding. Defaults to 0.
docstr: Forward function.
  Args:
      x (Tensor): Input feature map.
  
  Returns:
      Tensor: Output tensor after Avgpooling layer.
docstr_tok: ["Forward","function",".","Args",":","x","(","Tensor",")",":","Input","feature","map",".","Returns",":","Tensor",":","Output","tensor","after","Avgpooling","layer","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Forward function.
      Args:
          x (Tensor): Input feature map.
  
      Returns:
          Tensor: Output tensor after Avgpooling layer.
      """
      return self.model(x)
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from mmocr.registry import MODELS
label_desc: Use image node_id 2 for calling the AvgPool2d obj's underlying member method code with example usage: obj.forward(x) and returns: self