class_docstr: Squeeze and excitation layer, as per https://arxiv.org/pdf/1709.01507.pdf
class_docstr_tok: ["Squeeze","and","excitation","layer",",","as","per","https",":","\/\/arxiv.org\/pdf\/1709.01507.pdf"]
code: def forward(self, x):
      x_squeezed = self.avgpool(x)
      x_excited = self.excitation(x_squeezed)
      x_scaled = x * x_excited
      return x_scaled
  
imports: from typing import Optional
  import torch.nn
label_desc: Use image node_id 2 for calling the SqueezeAndExcitationLayer obj's underlying member method code with example usage: obj.forward(x) and returns: x_scaled