class_docstr: ResNext model from
  `"Aggregated Residual Transformations for Deep Neural Network"
  <http://arxiv.org/abs/1611.05431>`_ paper.
  
  Parameters
  ----------
  layers : list of int
      Numbers of layers in each block
  cardinality: int
      Number of groups
  bottleneck_width: int
      Width of bottleneck block
  classes : int, default 1000
      Number of classification classes.
  last_gamma : bool, default False
      Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
  use_se : bool, default False
      Whether to use Squeeze-and-Excitation module
  deep_stem : bool, default False
      Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
  stem_width : int, default 64
      Width of the stem intermediate layer.
  avg_down : bool, default False
      Whether to use average pooling for projection skip connection between stages/downsample.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
code: def _make_layer(
      self,
      channels,
      num_layers,
      stride,
      last_gamma,
      use_se,
      avg_down,
      stage_index,
      norm_layer=BatchNorm,
      norm_kwargs=None,
  ):
      layer = nn.HybridSequential(prefix="stage%d_" % stage_index)
      with layer.name_scope():
          layer.add(
              Block(
                  channels,
                  self.cardinality,
                  self.bottleneck_width,
                  stride,
                  True,
                  last_gamma=last_gamma,
                  use_se=use_se,
                  avg_down=avg_down,
                  prefix="",
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          for _ in range(num_layers - 1):
              layer.add(
                  Block(
                      channels,
                      self.cardinality,
                      self.bottleneck_width,
                      1,
                      False,
                      last_gamma=last_gamma,
                      use_se=use_se,
                      prefix="",
                      norm_layer=norm_layer,
                      norm_kwargs=norm_kwargs,
                  )
              )
      return layer
  
imports: from __future__ import division
  import os
  import math
  from mxnet import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 2 for calling the ResNext obj's underlying member method code with example usage: obj._make_layer(channels, num_layers, stride, last_gamma, use_se, avg_down, stage_index, norm_layer, norm_kwargs) and returns: layer