code: def __init__(
      self,
      in_channels,
      channels,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(_FCNHead, self).__init__()
      with self.name_scope():
          self.block = nn.HybridSequential()
          inter_channels = in_channels // 4
          with self.block.name_scope():
              self.block.add(
                  nn.Conv2D(
                      in_channels=in_channels,
                      channels=inter_channels,
                      kernel_size=3,
                      padding=1,
                      use_bias=False,
                  )
              )
              self.block.add(
                  norm_layer(
                      in_channels=inter_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=inter_channels,
                      channels=channels,
                      kernel_size=1,
                  )
              )
  
file_path: gluon-cv/gluoncv/model_zoo/fcn.py
imports: from __future__ import division
  from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from .segbase import SegBaseModel
label_desc: Use image node_id 1 to create a new _FCNHead object from inherited base classes: HybridBlock with example: obj = _FCNHead(in_channels, channels, norm_layer, norm_kwargs)