class_docstr: 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
  stem_filters : int
      Number of filters in stem layers
  filters_multiplier : int
      The filter multiplier for stem layers
  classes: int, default 1000
      Number of classification classes
  use_aux : bool, default True
      Whether to use auxiliary classifier when training
  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`.
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code: def hybrid_forward(self, F, x):
      x_conv0 = self.conv0(x)
      x_stem_0 = self.cell_stem_0(x_conv0)
      x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
  
      x = x_stem_1
      x_prev = x_stem_0
      for cell in self.norm_1._children.values():
          x, x_prev = cell(x, x_prev)
      x, x_prev = self.reduction_cell_0(x, x_prev)
      for cell in self.norm_2._children.values():
          x, x_prev = cell(x, x_prev)
      if self.out_aux:
          x_aux = F.contrib.AdaptiveAvgPooling2D(x, output_size=5)
          x_aux = self.out_aux(x_aux)
      x, x_prev = self.reduction_cell_1(x, x_prev)
      for cell in self.norm_3._children.values():
          x, x_prev = cell(x, x_prev)
  
      x = self.out(x)
      if self.out_aux:
          return x, x_aux
      else:
          return x
  
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 2 for calling the NASNetALarge obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x,  x_aux, x