class_docstr: HRNet for Segmentation
      
class_docstr_tok: ["HRNet","for","Segmentation"]
code: def _make_head(
      self,
      pre_stage_channels,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      num_classes=19,
      final_conv_kernel=1,
  ):
      last_inp_channels = np.int(np.sum(pre_stage_channels))
  
      last_layer = nn.HybridSequential()
      last_layer.add(
          nn.Conv2D(
              last_inp_channels, 1, 1, 0, in_channels=last_inp_channels
          ),
          norm_layer(**({} if norm_kwargs is None else norm_kwargs)),
          nn.Activation("relu"),
          nn.Conv2D(
              num_classes,
              final_conv_kernel,
              1,
              padding=1 if final_conv_kernel == 3 else 0,
          ),
      )
      return last_layer
  
imports: import numpy
  from mxnet.gluon import contrib
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.context import cpu
  from .resnet import BasicBlockV1, BottleneckV1, _conv3x3
label_desc: Use image node_id 3 for calling the HighResolutionSegNet obj's underlying member method code with example usage: obj._make_head(pre_stage_channels, norm_layer, norm_kwargs, num_classes, final_conv_kernel) and returns: last_layer