class_docstr: Base class for classification and segmentation
      
class_docstr_tok: ["Base","class","for","classification","and","segmentation"]
code: def _make_stage(
      self, layer_config, num_inchannels, multi_scale_output=True
  ):
      num_modules = layer_config[0]
      num_blocks = layer_config[2]
      num_branches = len(num_blocks)
      num_channels = layer_config[3]
      block = BLOCKS_DICT[layer_config[1]]
      fuse_method = layer_config[4]
  
      blocks = nn.HybridSequential()
      for i in range(num_modules):
          # multi_scale_output is only used last module
          if not multi_scale_output and i == num_modules - 1:
              reset_multi_scale_output = False
          else:
              reset_multi_scale_output = True
          hrm = HighResolutionModule(
              num_branches,
              block,
              num_blocks,
              num_channels,
              fuse_method,
              num_inchannels,
              reset_multi_scale_output,
              self.stage_interp_type,
          )
          blocks.add(hrm)
          num_inchannels = hrm.get_num_inchannels()
  
      return blocks, num_inchannels
  
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 4 for calling the HighResolutionBaseNet obj's underlying member method code with example usage: obj._make_stage(layer_config, num_inchannels, multi_scale_output) and returns: blocks,  num_inchannels