parameters: (pretrained=False, progress=True, **kwargs)
return_statement: return model
docstring: Constructs a MobileNetV2 architecture from
      `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
docstring_summary: Constructs a MobileNetV2 architecture from
      `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
docstring_tokens: Constructs a MobileNetV2 architecture from MobileNetV2 : Inverted Residuals and Linear Bottlenecks <https : // arxiv . org / abs / 1801 . 04381 > _ . Args : pretrained ( bool ) : If True returns a model pre - trained on ImageNet progress ( bool ) : If True displays a progress bar of the download to stderr
function: def mobilenetv2_rnnpool(pretrained=False, progress=True, **kwargs):
      """
      Constructs a MobileNetV2 architecture from
      `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      model = MobileNetV2(**kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
function_tokens: def mobilenetv2_rnnpool ( pretrained = False , progress = True , * * kwargs ) : model = MobileNetV2 ( * * kwargs ) if pretrained : state_dict = load_state_dict_from_url ( model_urls [ 'mobilenet_v2' ] , progress = progress ) model . load_state_dict ( state_dict ) return model