class_docstr: Base class for classification and segmentation
      
class_docstr_tok: ["Base","class","for","classification","and","segmentation"]
code: def _make_layer(self, block, planes, blocks, inplanes=0, stride=1):
      downsample = stride != 1 or inplanes != planes * block.expansion
  
      layers = nn.HybridSequential()
      layers.add(
          block(planes * block.expansion, stride, downsample, inplanes)
      )
      for i in range(1, blocks):
          layers.add(
              block(planes * block.expansion, 1, False, inplanes)
          )
  
      return layers
  
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 HighResolutionBaseNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, inplanes, stride) and returns: layers