class_docstr: HRNet for Classification
      
class_docstr_tok: ["HRNet","for","Classification"]
code: def _make_head(
      self, pre_stage_channels, norm_layer=BatchNorm, norm_kwargs=None
  ):
      head_block = BLOCKS_DICT["BOTTLENECK"]
      head_channels = [32, 64, 128, 256]
  
      incre_blocks = nn.HybridSequential()
      for i, channels in enumerate(pre_stage_channels):
          incre_block = self._make_layer(
              head_block, head_channels[i], 1, channels, stride=1
          )
          incre_blocks.add(incre_block)
  
      downsamp_blocks = nn.HybridSequential()
      for i in range(len(pre_stage_channels) - 1):
          in_channels = head_channels[i] * head_block.expansion
          out_channels = head_channels[i + 1] * head_block.expansion
  
          downsamp_block = nn.HybridSequential()
          downsamp_block.add(
              nn.Conv2D(out_channels, 3, 2, 1, in_channels=in_channels),
              norm_layer(
                  **({} if norm_kwargs is None else norm_kwargs)
              ),
              nn.Activation("relu"),
          )
          downsamp_blocks.add(downsamp_block)
  
      final_layer = nn.HybridSequential()
      final_layer.add(
          nn.Conv2D(
              2048,
              1,
              1,
              0,
              in_channels=head_channels[3] * head_block.expansion,
          ),
          norm_layer(**({} if norm_kwargs is None else norm_kwargs)),
          nn.Activation("relu"),
      )
  
      return incre_blocks, downsamp_blocks, final_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 HighResolutionClsNet obj's underlying member method code with example usage: obj._make_head(pre_stage_channels, norm_layer, norm_kwargs) and returns: incre_blocks,  downsamp_blocks,  final_layer