code: def get_mobilenet_v2(
      multiplier,
      pretrained=False,
      ctx=cpu(),
      root="~/.mxnet/models",
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      r"""MobileNetV2 model from the
      `"Inverted Residuals and Linear Bottlenecks:
        Mobile Networks for Classification, Detection and Segmentation"
      <https://arxiv.org/abs/1801.04381>`_ paper.
  
      Parameters
      ----------
      multiplier : float
          The width multiplier for controlling the model size. Only multipliers that are no
          less than 0.25 are supported. The actual number of channels is equal to the original
          channel size multiplied by this multiplier.
      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.
      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 = MobileNetV2(
          multiplier,
          norm_layer=norm_layer,
          norm_kwargs=norm_kwargs,
          **kwargs,
      )
  
      if pretrained:
          from .model_store import get_model_file
  
          version_suffix = "{0:.2f}".format(multiplier)
          if version_suffix in ("1.00", "0.50"):
              version_suffix = version_suffix[:-1]
          net.load_parameters(
              get_model_file(
                  "mobilenetv2_%s" % version_suffix,
                  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 mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from ..nn import ReLU6
label_desc: Use image node_id 4 for calling a global function with example usage: get_mobilenet_v2(multiplier, pretrained, ctx, root, norm_layer, norm_kwargs) and returns: net