code: def get_nasnet(
      repeat=6,
      penultimate_filters=4032,
      pretrained=False,
      ctx=cpu(),
      root=os.path.join("~", ".mxnet", "models"),
      **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`.
      """
      assert (
          repeat >= 2
      ), "Invalid number of repeat: %d. It should be at least two" % (
          repeat
      )
      net = NASNetALarge(
          repeat=repeat,
          penultimate_filters=penultimate_filters,
          **kwargs,
      )
      if pretrained:
          from .model_store import get_model_file
  
          net.load_parameters(
              get_model_file(
                  "nasnet_%d_%d" % (repeat, penultimate_filters),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      return net
  
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 1 for calling a global function with example usage: get_nasnet(repeat, penultimate_filters, pretrained, ctx, root) and returns: net