class_docstr: GoogleNet model from
  `"Going Deeper with Convolutions"
  <https://arxiv.org/abs/1409.4842>`_ paper.
  `"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"
  <https://arxiv.org/abs/1502.03167>`_ paper.
  
  Parameters
  ----------
  classes : int, default 1000
      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`.
  partial_bn : bool, default False
      Freeze all batch normalization layers during training except the first layer.
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code: def hybrid_forward(self, F, x):
      x = self.conv1(x)
      x = self.maxpool1(x)
  
      x = self.conv2(x)
      x = self.conv3(x)
      x = self.maxpool2(x)
  
      x = self.inception3a(x)
      x = self.inception3b(x)
      x = self.maxpool3(x)
  
      x = self.inception4a(x)
      if self.aux_logits:
          aux1 = self.aux1(x)
      x = self.inception4b(x)
      x = self.inception4c(x)
      x = self.inception4d(x)
      if self.aux_logits:
          aux2 = self.aux2(x)
      x = self.inception4e(x)
      x = self.maxpool4(x)
  
      x = self.inception5a(x)
      x = self.inception5b(x)
      x = self.head(x)
  
      if self.aux_logits:
          return (x, aux2, aux1)
      return x
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent
label_desc: Use image node_id 2 for calling the GoogLeNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x, x,  aux2,  aux1