code: def __init__(
      self,
      in_channels,
      inter_channels1,
      inter_channels2,
      norm_layer=nn.BatchNorm2d,
      norm_kwargs=None,
  ):
      super(_DenseASPPBlock, self).__init__()
      self.aspp_3 = _DenseASPPConv(
          in_channels,
          inter_channels1,
          inter_channels2,
          3,
          0.1,
          norm_layer,
          norm_kwargs,
      )
      self.aspp_6 = _DenseASPPConv(
          in_channels + inter_channels2 * 1,
          inter_channels1,
          inter_channels2,
          6,
          0.1,
          norm_layer,
          norm_kwargs,
      )
      self.aspp_12 = _DenseASPPConv(
          in_channels + inter_channels2 * 2,
          inter_channels1,
          inter_channels2,
          12,
          0.1,
          norm_layer,
          norm_kwargs,
      )
      self.aspp_18 = _DenseASPPConv(
          in_channels + inter_channels2 * 3,
          inter_channels1,
          inter_channels2,
          18,
          0.1,
          norm_layer,
          norm_kwargs,
      )
      self.aspp_24 = _DenseASPPConv(
          in_channels + inter_channels2 * 4,
          inter_channels1,
          inter_channels2,
          24,
          0.1,
          norm_layer,
          norm_kwargs,
      )
  
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 _DenseASPPBlock object from inherited base classes: nn with example: obj = _DenseASPPBlock(in_channels, inter_channels1, inter_channels2, norm_layer, norm_kwargs)