code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: int,
      padding: int,
      dilation: int,
      norm_layer=nn.BatchNorm2d,
      activation=ACT_RELU,
  ):
      super(ASPPModule, self).__init__()
      self.conv = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=1,
          padding=padding,
          dilation=dilation,
          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 ASPPModule object from inherited base classes: nn with example: obj = ASPPModule(in_channels, out_channels, kernel_size, padding, dilation, norm_layer, activation)