code: def __init__(self, in_channels, channels, se_ratio=12):
      super(SE, self).__init__()
      self.avg_pool = nn.AdaptiveAvgPool2D(1)
      self.fc = nn.Sequential(
          nn.Conv2D(
              in_channels,
              channels // se_ratio,
              kernel_size=1,
              padding=0,
          ),
          nn.BatchNorm2D(channels // se_ratio),
          nn.ReLU(),
          nn.Conv2D(
              channels // se_ratio, channels, kernel_size=1, padding=0
          ),
          nn.Sigmoid(),
      )
  
imports: import os
  from math import ceil
  from typing import Union
  import paddle
  import paddle.nn
  import numpy
  import paddlehub.vision.transforms
  from paddle import ParamAttr
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 1 to create a new SE object from inherited base classes: nn with example: obj = SE(in_channels, channels, se_ratio)