class_docstr: HRNet for Classification
      
class_docstr_tok: ["HRNet","for","Classification"]
code: def hybrid_forward(self, F, x):
      y_list = super(HighResolutionClsNet, self).hybrid_forward(F, x)
  
      # Classification Head
      y = self.incre_blocks[0](y_list[0])
      for i in range(len(self.downsamp_blocks)):
          y = self.incre_blocks[i + 1](
              y_list[i + 1]
          ) + self.downsamp_blocks[i](y)
  
      y = self.final_layer(y)
      y = self.avg(y)
      y = self.classifier(y)
  
      return y
  
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 2 for calling the HighResolutionClsNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: y