code: def get_symbol(
      num_classes,
      num_layers,
      image_shape,
      num_group=32,
      conv_workspace=256,
      dtype="float32",
      **kwargs,
  ):
      """
      Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
      Original author Wei Wu
      """
      image_shape = [int(dim) for dim in image_shape.split(",")]
      (nchannel, height, width) = image_shape
      if height <= 32:
          num_stages = 3
          if (num_layers - 2) % 9 == 0 and num_layers >= 164:
              per_unit = [(num_layers - 2) // 9]
              filter_list = [16, 64, 128, 256]
              bottle_neck = True
          elif (num_layers - 2) % 6 == 0 and num_layers < 164:
              per_unit = [(num_layers - 2) // 6]
              filter_list = [16, 16, 32, 64]
              bottle_neck = False
          else:
              raise ValueError(
                  "no experiments done on num_layers {}, you can do it yourself".format(
                      num_layers
                  )
              )
          units = per_unit * num_stages
      else:
          if num_layers >= 50:
              filter_list = [64, 256, 512, 1024, 2048]
              bottle_neck = True
          else:
              filter_list = [64, 64, 128, 256, 512]
              bottle_neck = False
          num_stages = 4
          if num_layers == 18:
              units = [2, 2, 2, 2]
          elif num_layers == 34:
              units = [3, 4, 6, 3]
          elif num_layers == 50:
              units = [3, 4, 6, 3]
          elif num_layers == 101:
              units = [3, 4, 23, 3]
          elif num_layers == 152:
              units = [3, 8, 36, 3]
          elif num_layers == 200:
              units = [3, 24, 36, 3]
          elif num_layers == 269:
              units = [3, 30, 48, 8]
          else:
              raise ValueError(
                  "no experiments done on num_layers {}, you can do it yourself".format(
                      num_layers
                  )
              )
  
      return resnext(
          units=units,
          num_stages=num_stages,
          filter_list=filter_list,
          num_classes=num_classes,
          num_group=num_group,
          image_shape=image_shape,
          bottle_neck=bottle_neck,
          workspace=conv_workspace,
          dtype=dtype,
      )
  
imports: import mxnet
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: get_symbol(num_classes, num_layers, image_shape, num_group, conv_workspace, dtype) and returns: resnext