code: def __init__(
      self,
      in_channels,
      nclass,
      aux=True,
      norm_layer=nn.BatchNorm2d,
      norm_kwargs=None,
      **kwargs,
  ):
      super(_DAHead, self).__init__()
      self.aux = aux
      inter_channels = in_channels // 4
      self.conv_p1 = nn.Sequential(
          nn.Conv2d(
              in_channels, inter_channels, 3, padding=1, bias=False
          ),
          norm_layer(
              inter_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
          nn.ReLU(True),
      )
      self.conv_c1 = nn.Sequential(
          nn.Conv2d(
              in_channels, inter_channels, 3, padding=1, bias=False
          ),
          norm_layer(
              inter_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
          nn.ReLU(True),
      )
      self.pam = _PositionAttentionModule(inter_channels, **kwargs)
      self.cam = _ChannelAttentionModule(**kwargs)
      self.conv_p2 = nn.Sequential(
          nn.Conv2d(
              inter_channels, inter_channels, 3, padding=1, bias=False
          ),
          norm_layer(
              inter_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
          nn.ReLU(True),
      )
      self.conv_c2 = nn.Sequential(
          nn.Conv2d(
              inter_channels, inter_channels, 3, padding=1, bias=False
          ),
          norm_layer(
              inter_channels,
              **({} if norm_kwargs is None else norm_kwargs),
          ),
          nn.ReLU(True),
      )
      self.out = nn.Sequential(
          nn.Dropout(0.1), nn.Conv2d(inter_channels, nclass, 1)
      )
      if aux:
          self.conv_p3 = nn.Sequential(
              nn.Dropout(0.1), nn.Conv2d(inter_channels, nclass, 1)
          )
          self.conv_c3 = nn.Sequential(
              nn.Dropout(0.1), nn.Conv2d(inter_channels, nclass, 1)
          )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
label_desc: Use image node_id 1 to create a new _DAHead object from inherited base classes: nn with example: obj = _DAHead(in_channels, nclass, aux, norm_layer, norm_kwargs)