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.
code: def __init__(
      self,
      kernel_size: Union[int, Tuple[int]],
      stride: Optional[Union[int, Tuple[int]]] = None,
      padding: Union[int, Tuple[int]] = 0,
      **kwargs,
  ) -> None:
      super().__init__()
      self.model = nn.AvgPool2d(kernel_size, stride, padding)
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from mmocr.registry import MODELS
label_desc: Use image node_id 1 to create a new AvgPool2d object from inherited base classes: nn with example: obj = AvgPool2d(kernel_size, stride, padding)