code: def get_mobilenet_v3(
      model_name,
      multiplier=1.0,
      pretrained=False,
      ctx=cpu(),
      root="~/.mxnet/models",
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      r"""MobileNet model from the
      `"Searching for MobileNetV3"
      <https://arxiv.org/abs/1905.02244>`_ paper.
  
      Parameters
      ----------
      model_name : string
          The name of mobilenetv3 models, large and small are supported.
      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`.
      """
      if model_name == "large":
          cfg = [
              # k, exp, c,  se,     nl,  s,
              [3, 16, 16, False, "relu", 1],
              [3, 64, 24, False, "relu", 2],
              [3, 72, 24, False, "relu", 1],
              [5, 72, 40, True, "relu", 2],
              [5, 120, 40, True, "relu", 1],
              [5, 120, 40, True, "relu", 1],
              [3, 240, 80, False, "hard_swish", 2],
              [3, 200, 80, False, "hard_swish", 1],
              [3, 184, 80, False, "hard_swish", 1],
              [3, 184, 80, False, "hard_swish", 1],
              [3, 480, 112, True, "hard_swish", 1],
              [3, 672, 112, True, "hard_swish", 1],
              [5, 672, 160, True, "hard_swish", 2],
              [5, 960, 160, True, "hard_swish", 1],
              [5, 960, 160, True, "hard_swish", 1],
          ]
          cls_ch_squeeze = 960
          cls_ch_expand = 1280
      elif model_name == "small":
          cfg = [
              # k, exp, c,  se,     nl,  s,
              [3, 16, 16, True, "relu", 2],
              [3, 72, 24, False, "relu", 2],
              [3, 88, 24, False, "relu", 1],
              [5, 96, 40, True, "hard_swish", 2],
              [5, 240, 40, True, "hard_swish", 1],
              [5, 240, 40, True, "hard_swish", 1],
              [5, 120, 48, True, "hard_swish", 1],
              [5, 144, 48, True, "hard_swish", 1],
              [5, 288, 96, True, "hard_swish", 2],
              [5, 576, 96, True, "hard_swish", 1],
              [5, 576, 96, True, "hard_swish", 1],
          ]
          cls_ch_squeeze = 576
          cls_ch_expand = 1280
      else:
          raise NotImplementedError
      net = _MobileNetV3(
          cfg,
          cls_ch_squeeze,
          cls_ch_expand,
          multiplier=multiplier,
          final_drop=0.2,
          norm_layer=norm_layer,
          **kwargs,
      )
      if pretrained:
          from .model_store import get_model_file
  
          net.load_parameters(
              get_model_file(
                  "mobilenetv3_%s" % model_name,
                  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 __future__ import division
  import numpy
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
  from mxnet.context import cpu
  from ..nn import ReLU6, HardSigmoid, HardSwish
label_desc: Use image node_id 2 for calling a global function with example usage: get_mobilenet_v3(model_name, multiplier, pretrained, ctx, root, norm_layer, norm_kwargs) and returns: net