parameters: (pretrained=False, scale=1.0, **kwargs)
return_statement: return model
docstring: MobileNetV2
      
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
          scale: (float): scale of channels in each layer. Default: 1.0.
  
      Examples:
          .. code-block:: python
  
              from paddle.vision.models import mobilenet_v2
  
              # build model
              model = mobilenet_v2()
  
              # build model and load imagenet pretrained weight
              # model = mobilenet_v2(pretrained=True)
  
              # build mobilenet v2 with scale=0.5
              model = mobilenet_v2(scale=0.5)
docstring_summary: MobileNetV2
      
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
          scale: (float): scale of channels in each layer. Default: 1.0.
docstring_tokens: MobileNetV2 Args : pretrained ( bool ) : If True returns a model pre - trained on ImageNet . Default : False . scale : ( float ) : scale of channels in each layer . Default : 1 . 0 .
function: def mobilenet_v2(pretrained=False, scale=1.0, **kwargs):
      """MobileNetV2
      
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
          scale: (float): scale of channels in each layer. Default: 1.0.
  
      Examples:
          .. code-block:: python
  
              from paddle.vision.models import mobilenet_v2
  
              # build model
              model = mobilenet_v2()
  
              # build model and load imagenet pretrained weight
              # model = mobilenet_v2(pretrained=True)
  
              # build mobilenet v2 with scale=0.5
              model = mobilenet_v2(scale=0.5)
      """
      model = _mobilenet(
          'mobilenetv2_' + str(scale), pretrained, scale=scale, **kwargs)
      return model
function_tokens: def mobilenet_v2 ( pretrained = False , scale = 1.0 , * * kwargs ) : model = _mobilenet ( 'mobilenetv2_' + str ( scale ) , pretrained , scale = scale , * * kwargs ) return model