class_docstr: HRNet for Segmentation
      
class_docstr_tok: ["HRNet","for","Segmentation"]
code: def hybrid_forward(self, F, x):
      y_list = super(HighResolutionSegNet, self).hybrid_forward(F, x)
  
      # Upsampling
      x1 = F.contrib.BilinearResize2D(
          y_list[1], like=y_list[0], mode="like", align_corners=False
      )
      x2 = F.contrib.BilinearResize2D(
          y_list[2], like=y_list[0], mode="like", align_corners=False
      )
      x3 = F.contrib.BilinearResize2D(
          y_list[3], like=y_list[0], mode="like", align_corners=False
      )
  
      y = F.concat(y_list[0], x1, x2, x3, dim=1)
  
      y = self.last_layer(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 HighResolutionSegNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: y