code: def resnext50_32x4d(**kwargs):
      r"""ResNext50 32x4d model from
      `"Aggregated Residual Transformations for Deep Neural Network"
      <http://arxiv.org/abs/1611.05431>`_ paper.
  
      Parameters
      ----------
      cardinality: int
          Number of groups
      bottleneck_width: int
          Width of bottleneck block
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      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`.
      """
      kwargs["use_se"] = False
      return get_resnext(50, 32, 4, **kwargs)
  
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 a global function with example usage: resnext50_32x4d() and returns: get_resnext