class_docstr: interp_type can be 'nearest'/'bilinear'/'bilinear_like'
class_docstr_tok: ["interp_type","can","be","'nearest'\/'bilinear'\/'bilinear_like","'"]
code: def _make_one_branch(
      self, branch_index, block, num_blocks, num_channels, stride=1
  ):
      downsample = (
          stride != 1
          or self.num_inchannels[branch_index]
          != num_channels[branch_index] * block.expansion
      )
  
      layers = nn.HybridSequential()
      layers.add(
          block(
              num_channels[branch_index] * block.expansion,
              stride,
              downsample,
              self.num_inchannels[branch_index],
          )
      )
      self.num_inchannels[branch_index] = (
          num_channels[branch_index] * block.expansion
      )
      for i in range(1, num_blocks[branch_index]):
          layers.add(
              block(
                  num_channels[branch_index] * block.expansion,
                  1,
                  False,
                  self.num_inchannels[branch_index],
              )
          )
  
      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 2 for calling the HighResolutionModule obj's underlying member method code with example usage: obj._make_one_branch(branch_index, block, num_blocks, num_channels, stride) and returns: layers