code: def __init__(self, in_channels, out_channels, reduction=4):
      super().__init__()
      self.classifier = nn.Conv2d(
          in_channels, out_channels, kernel_size=1
      )
      self.weight_generator = nn.Sequential(
          nn.BatchNorm2d(in_channels),
          nn.Conv2d(
              in_channels, in_channels // reduction, kernel_size=1
          ),
          nn.ReLU(True),
          nn.Conv2d(
              in_channels // reduction, out_channels, kernel_size=1
          ),
          nn.Sigmoid(),
      )
  
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 1 to create a new MILCustomPoolingModule object from inherited base classes: nn with example: obj = MILCustomPoolingModule(in_channels, out_channels, reduction)