class_docstr: ResNext model from `"Aggregated Residual Transformations for Deep Neural Networks"
  <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 10
      Number of classification classes.
  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`.
class_docstr_tok: ["ResNext","model","from","`","``","Aggregated","Residual","Transformations","for","Deep","Neural","Networks","''","<","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","10","Number","of","classification","classes",".","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_layer,
      stride,
      stage_index,
      norm_layer=BatchNorm,
      norm_kwargs=None,
  ):
      layer = nn.HybridSequential(prefix="stage%d_" % stage_index)
      with layer.name_scope():
          layer.add(
              CIFARBlock(
                  channels,
                  self.cardinality,
                  self.bottleneck_width,
                  stride,
                  True,
                  prefix="",
                  norm_layer=norm_layer,
                  norm_kwargs=norm_kwargs,
              )
          )
          for _ in range(num_layer - 1):
              layer.add(
                  CIFARBlock(
                      channels,
                      self.cardinality,
                      self.bottleneck_width,
                      1,
                      False,
                      prefix="",
                      norm_layer=norm_layer,
                      norm_kwargs=norm_kwargs,
                  )
              )
      return layer
  
file_path: gluon-cv/gluoncv/model_zoo/cifarresnext.py
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 CIFARResNext obj's underlying member method code with example usage: obj._make_layer(channels, num_layer, stride, stage_index, norm_layer, norm_kwargs) and returns: layer