class_docstr: Base class for classification and segmentation
      
class_docstr_tok: ["Base","class","for","classification","and","segmentation"]
code: def hybrid_forward(self, F, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = F.relu(x)
      x = self.conv2(x)
      x = self.bn2(x)
      x = F.relu(x)
      x = self.layer1(x)
      x_list = []
  
      for i in range(len(self.stage2_cfg[2])):
          x_list.append(self.transition1[i](x))
  
      y_list = x_list
      for s in self.stage2:
          y_list = s(*y_list)
  
      x_list = []
      for i in range(len(self.stage3_cfg[2])):
          if i < len(self.stage2_cfg[2]):
              x_list.append(self.transition2[i](y_list[i]))
          else:
              x_list.append(self.transition2[i](y_list[-1]))
  
      y_list = x_list
      for s in self.stage3:
          y_list = s(*y_list)
  
      x_list = []
      for i in range(len(self.stage4_cfg[2])):
          if i < len(self.stage3_cfg[2]):
              x_list.append(self.transition3[i](y_list[i]))
          else:
              x_list.append(self.transition3[i](y_list[-1]))
  
      y_list = x_list
      for s in self.stage4:
          y_list = s(*y_list)
  
      return y_list
  
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 5 for calling the HighResolutionBaseNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: y_list