class_docstr: Global Weighted Average Pooling from paper "Global Weighted Average
  Pooling Bridges Pixel-level Localization and Image-level Classification"
code: def forward(self, x):  # skipcq: PYL-W0221
      input_x = x
      x = self.fscore(x)
      x = self.norm(x)
      x = x * input_x
      x = x.sum(dim=[2, 3], keepdim=not self.flatten)
      return x
  
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 4 for calling the GlobalWeightedAvgPool2d obj's underlying member method code with example usage: obj.forward(x) and returns: x