code: def nasnet_5_1538(**kwargs):
      r"""NASNet A model from
      `"Learning Transferable Architectures for Scalable Image Recognition"
      <https://arxiv.org/abs/1707.07012>`_ paper
  
      Parameters
      ----------
      repeat : int
          Number of cell repeats
      penultimate_filters : int
          Number of filters in the penultimate layer of the network
      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`.
      """
      return get_nasnet(repeat=5, penultimate_filters=1538, **kwargs)
  
imports: from __future__ import division
  import os
  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 3 for calling a global function with example usage: nasnet_5_1538() and returns: get_nasnet