code: def resnext(
      units,
      num_stages,
      filter_list,
      num_classes,
      num_group,
      image_shape,
      bottle_neck=True,
      bn_mom=0.9,
      workspace=256,
      dtype="float32",
      memonger=False,
  ):
      """Return ResNeXt symbol of
      Parameters
      ----------
      units : list
          Number of units in each stage
      num_stages : int
          Number of stage
      filter_list : list
          Channel size of each stage
      num_classes : int
          Ouput size of symbol
      num_groupes: int
      Number of conv groups
      dataset : str
          Dataset type, only cifar10 and imagenet supports
      workspace : int
          Workspace used in convolution operator
      dtype : str
          Precision (float32 or float16)
      """
      num_unit = len(units)
      assert num_unit == num_stages
      data = mx.sym.Variable(name="data")
      if dtype == "float32":
          data = mx.sym.identity(data=data, name="id")
      else:
          if dtype == "float16":
              data = mx.sym.Cast(data=data, dtype=np.float16)
      data = mx.sym.BatchNorm(
          data=data,
          fix_gamma=True,
          eps=2e-5,
          momentum=bn_mom,
          name="bn_data",
      )
      (nchannel, height, width) = image_shape
      if height <= 32:  # such as cifar10
          body = mx.sym.Convolution(
              data=data,
              num_filter=filter_list[0],
              kernel=(3, 3),
              stride=(1, 1),
              pad=(1, 1),
              no_bias=True,
              name="conv0",
              workspace=workspace,
          )
      else:  # often expected to be 224 such as imagenet
          body = mx.sym.Convolution(
              data=data,
              num_filter=filter_list[0],
              kernel=(7, 7),
              stride=(2, 2),
              pad=(3, 3),
              no_bias=True,
              name="conv0",
              workspace=workspace,
          )
          body = mx.sym.BatchNorm(
              data=body,
              fix_gamma=False,
              eps=2e-5,
              momentum=bn_mom,
              name="bn0",
          )
          body = mx.sym.Activation(
              data=body, act_type="relu", name="relu0"
          )
          body = mx.sym.Pooling(
              data=body,
              kernel=(3, 3),
              stride=(2, 2),
              pad=(1, 1),
              pool_type="max",
          )
  
      for i in range(num_stages):
          body = residual_unit(
              body,
              filter_list[i + 1],
              (1 if i == 0 else 2, 1 if i == 0 else 2),
              False,
              name="stage%d_unit%d" % (i + 1, 1),
              bottle_neck=bottle_neck,
              num_group=num_group,
              bn_mom=bn_mom,
              workspace=workspace,
              memonger=memonger,
          )
          for j in range(units[i] - 1):
              body = residual_unit(
                  body,
                  filter_list[i + 1],
                  (1, 1),
                  True,
                  name="stage%d_unit%d" % (i + 1, j + 2),
                  bottle_neck=bottle_neck,
                  num_group=num_group,
                  bn_mom=bn_mom,
                  workspace=workspace,
                  memonger=memonger,
              )
  
      pool1 = mx.sym.Pooling(
          data=body,
          global_pool=True,
          kernel=(7, 7),
          pool_type="avg",
          name="pool1",
      )
      flat = mx.sym.Flatten(data=pool1)
      fc1 = mx.sym.FullyConnected(
          data=flat, num_hidden=num_classes, name="fc1"
      )
      if dtype == "float16":
          fc1 = mx.sym.Cast(data=fc1, dtype=np.float32)
      return mx.sym.SoftmaxOutput(data=fc1, name="softmax")
  
imports: import mxnet
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: resnext(units, num_stages, filter_list, num_classes, num_group, image_shape, bottle_neck, bn_mom, workspace, dtype, memonger) and returns: mx