code: def __init__(
      self,
      in_channels=64,
      channels=64,
      nclass=19,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(_auxHead, self).__init__()
      with self.name_scope():
          self.block = nn.HybridSequential()
          with self.block.name_scope():
              self.block.add(
                  nn.Conv2D(
                      in_channels=in_channels,
                      channels=channels,
                      kernel_size=3,
                      padding=1,
                      use_bias=False,
                  )
              )
              self.block.add(
                  norm_layer(
                      in_channels=channels,
                      **({} 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=channels,
                      channels=nclass,
                      kernel_size=1,
                  )
              )
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new _auxHead object from inherited base classes: HybridBlock with example: obj = _auxHead(in_channels, channels, nclass, norm_layer, norm_kwargs)