code: def __init__(
      self,
      in_channels,
      nclass,
      norm_layer=nn.BatchNorm2d,
      norm_kwargs=None,
      **kwargs,
  ):
      super(_DenseASPPHead, self).__init__()
      self.dense_aspp_block = _DenseASPPBlock(
          in_channels, 256, 64, norm_layer, norm_kwargs
      )
      self.block = nn.Sequential(
          nn.Dropout(0.1), nn.Conv2d(in_channels + 5 * 64, nclass, 1)
      )
  
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 _DenseASPPHead object from inherited base classes: nn with example: obj = _DenseASPPHead(in_channels, nclass, norm_layer, norm_kwargs)