class_docstr: Squeeze-and-Excitation (SE) block: AvgPool, FC, Act, FC, Sigmoid.
class_docstr_tok: ["Squeeze-and-Excitation","(","SE",")","block",":","AvgPool",",","FC",",","Act",",","FC",",","Sigmoid","."]
code: def __init__(self, w_in, w_se, activation_class):
      super().__init__()
      self.avg_pool = gap2d()
      self.f_ex = nn.Sequential(
          conv2d(w_in, w_se, 1, bias=True),
          activation_class(),
          conv2d(w_se, w_in, 1, bias=True),
          nn.Sigmoid(),
      )
  
imports: import numpy
  from torch import nn
  from detectron2.layers import CNNBlockBase, ShapeSpec, get_norm
  from .backbone import Backbone
label_desc: Use image node_id 1 to create a new SE object from inherited base classes: nn with example: obj = SE(w_in, w_se, activation_class)