code: def __init__(
      self,
      in_channels: int,
      atrous_rates: List[int],
      out_channels: int = 256,
  ) -> None:
      super(ASPP, self).__init__()
      modules = [
          nn.Sequential(
              nn.Conv2d(in_channels, out_channels, 1, bias=False),
              nn.BatchNorm2d(out_channels),
              nn.ReLU(),
          )
      ]
  
      rates = tuple(atrous_rates)
      for rate in rates:
          modules.append(ASPPConv(in_channels, out_channels, rate))
  
      modules.append(ASPPPooling(in_channels, out_channels))
  
      self.convs = nn.ModuleList(modules)
  
      self.project = nn.Sequential(
          nn.Conv2d(
              len(self.convs) * out_channels,
              out_channels,
              1,
              bias=False,
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(),
          nn.Dropout(0.5),
      )
  
imports: from collections import OrderedDict
  from typing import Dict, List
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from .resnet_backbone import resnet50, resnet101
  from .mobilenet_backbone import mobilenet_v3_large
label_desc: Use image node_id 1 to create a new ASPP object from inherited base classes: nn with example: obj = ASPP(in_channels, atrous_rates, out_channels)