class_docstr: Squeeze-and-Excitation (SE) block: AvgPool, FC, Act, FC, Sigmoid.
code: def __init__(self, w_in, w_se):
      super(SE, self).__init__()
      self.avg_pool = gap2d(w_in)
      self.f_ex = nn.Sequential(
          conv2d(w_in, w_se, 1, bias=True),
          activation(),
          conv2d(w_se, w_in, 1, bias=True),
          nn.Sigmoid(),
      )
  
imports: import numpy
  import torch
  import torch.nn
  from pycls.core.config import cfg
  from torch.nn import Module
label_desc: Use image node_id 1 to create a new SE object from inherited base classes: Module with example: obj = SE(w_in, w_se)