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 __init__(
      self,
      layers,
      cardinality,
      bottleneck_width,
      classes=10,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(CIFARResNext, self).__init__(**kwargs)
      self.cardinality = cardinality
      self.bottleneck_width = bottleneck_width
      channels = 64
  
      with self.name_scope():
          self.features = nn.HybridSequential(prefix="")
          self.features.add(
              nn.Conv2D(channels, 3, 1, 1, use_bias=False)
          )
          self.features.add(
              norm_layer(**({} if norm_kwargs is None else norm_kwargs))
          )
          self.features.add(nn.Activation("relu"))
  
          for i, num_layer in enumerate(layers):
              stride = 1 if i == 0 else 2
              self.features.add(
                  self._make_layer(
                      channels,
                      num_layer,
                      stride,
                      i + 1,
                      norm_layer=norm_layer,
                      norm_kwargs=norm_kwargs,
                  )
              )
              channels *= 2
          self.features.add(nn.GlobalAvgPool2D())
  
          self.output = nn.Dense(classes)
  
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 1 to create a new CIFARResNext object from inherited base classes: HybridBlock with example: obj = CIFARResNext(layers, cardinality, bottleneck_width, classes, norm_layer, norm_kwargs)