code: def mobilenet_v2_0_5(**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
      ----------
      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.
      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`.
      """
      return get_mobilenet_v2(0.5, **kwargs)
  
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 10 for calling a global function with example usage: mobilenet_v2_0_5() and returns: get_mobilenet_v2