code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      norm_layer=nn.BatchNorm2d,
      activation: str = ACT_RELU,
  ):
      super().__init__()
      self.pooling = nn.AdaptiveAvgPool2d(1)
      self.conv = nn.Conv2d(in_channels, out_channels, 1, bias=False)
      self.abn = nn.Sequential(
          OrderedDict(
              [
                  ("norm", norm_layer(out_channels)),
                  (
                      "act",
                      instantiate_activation_block(
                          activation, inplace=True
                      ),
                  ),
              ]
          )
      )
  
imports: from collections import OrderedDict
  from typing import Union, Type
  import torch
  from torch import nn, Tensor
  from pytorch_toolbelt.modules import ACT_RELU, instantiate_activation_block, DepthwiseSeparableConv2d
label_desc: Use image node_id 1 to create a new ASPPPooling object from inherited base classes: nn with example: obj = ASPPPooling(in_channels, out_channels, norm_layer, activation)