code: def __init__(
      self,
      in_channels,
      inter_channels,
      out_channels,
      atrous_rate,
      drop_rate=0.1,
      norm_layer=nn.BatchNorm2d,
      norm_kwargs=None,
  ):
      super(_DenseASPPConv, self).__init__()
      self.add_module(
          "conv1", nn.Conv2d(in_channels, inter_channels, 1)
      )
      self.add_module(
          "bn1",
          norm_layer(
              inter_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
      )
      self.add_module("relu1", nn.ReLU(True))
      self.add_module(
          "conv2",
          nn.Conv2d(
              inter_channels,
              out_channels,
              3,
              dilation=atrous_rate,
              padding=atrous_rate,
          ),
      )
      self.add_module(
          "bn2",
          norm_layer(
              out_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
      )
      self.add_module("relu2", nn.ReLU(True))
      self.drop_rate = drop_rate
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.densenet import *
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 1 to create a new _DenseASPPConv object from inherited base classes: nn with example: obj = _DenseASPPConv(in_channels, inter_channels, out_channels, atrous_rate, drop_rate, norm_layer, norm_kwargs)