code: def __init__(
      self,
      nclass,
      c1_channels=128,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      height=128,
      width=128,
      **kwargs,
  ):
      super(_DeepLabHead, self).__init__()
      self._up_kwargs = {"height": height, "width": width}
      with self.name_scope():
          self.aspp = _ASPP(
              2048,
              [12, 24, 36],
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
              height=height // 2,
              width=width // 2,
              **kwargs,
          )
          self.c1_block = nn.HybridSequential()
          self.c1_block.add(
              nn.Conv2D(
                  in_channels=c1_channels,
                  channels=48,
                  kernel_size=3,
                  padding=1,
                  use_bias=False,
              )
          )
          self.c1_block.add(
              norm_layer(
                  in_channels=48,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          self.c1_block.add(nn.Activation("relu"))
  
          self.block = nn.HybridSequential()
          self.block.add(
              nn.Conv2D(
                  in_channels=304,
                  channels=256,
                  kernel_size=3,
                  padding=1,
                  use_bias=False,
              )
          )
          self.block.add(
              norm_layer(
                  in_channels=256,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          self.block.add(nn.Activation("relu"))
          self.block.add(nn.Dropout(0.5))
          self.block.add(
              nn.Conv2D(
                  in_channels=256,
                  channels=256,
                  kernel_size=3,
                  padding=1,
                  use_bias=False,
              )
          )
          self.block.add(
              norm_layer(
                  in_channels=256,
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
          self.block.add(nn.Activation("relu"))
          self.block.add(nn.Dropout(0.1))
          self.block.add(
              nn.Conv2D(in_channels=256, channels=nclass, kernel_size=1)
          )
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from mxnet import gluon
  from .fcn import _FCNHead
  from .xception import get_xcetption
label_desc: Use image node_id 1 to create a new _DeepLabHead object from inherited base classes: HybridBlock with example: obj = _DeepLabHead(nclass, c1_channels, norm_layer, norm_kwargs, height, width)