class_docstr: Global Weighted Average Pooling from paper "Global Weighted Average
  Pooling Bridges Pixel-level Localization and Image-level Classification"
code: def __init__(self, features: int, flatten=False):
      super().__init__()
      self.conv = nn.Conv2d(features, 1, kernel_size=1, bias=True)
      self.flatten = flatten
  
imports: from typing import Union, Dict
  import torch
  import torch.nn.functional
  from torch.nn.modules.module import _IncompatibleKeys
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new GlobalWeightedAvgPool2d object from inherited base classes: nn with example: obj = GlobalWeightedAvgPool2d(features, flatten)