code: def googlenet(
      classes=1000,
      pretrained=False,
      pretrained_base=True,
      ctx=cpu(),
      dropout_ratio=0.4,
      aux_logits=False,
      root="~/.mxnet/models",
      partial_bn=False,
      **kwargs,
  ):
      r"""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
      ----------
      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_HOME/models
          Location for keeping the model parameters.
      partial_bn : bool, default False
          Freeze all batch normalization layers during training except the first layer.
      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`.
      """
  
      net = GoogLeNet(
          classes=classes,
          partial_bn=partial_bn,
          pretrained_base=pretrained_base,
          dropout_ratio=dropout_ratio,
          aux_logits=aux_logits,
          ctx=ctx,
          **kwargs,
      )
      if pretrained:
          from .model_store import get_model_file
  
          net.load_parameters(
              get_model_file("googlenet", tag=pretrained, root=root),
              ctx=ctx,
              cast_dtype=True,
          )
          from ..data import ImageNet1kAttr
  
          attrib = ImageNet1kAttr()
          net.synset = attrib.synset
          net.classes = attrib.classes
          net.classes_long = attrib.classes_long
      return net
  
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 13 for calling a global function with example usage: googlenet(classes, pretrained, pretrained_base, ctx, dropout_ratio, aux_logits, root, partial_bn) and returns: net