parameters: (pretrained=False, **kwargs)
return_statement: return _shufflenet_v2(
          "shufflenet_v2_x0_5", scale=0.5, pretrained=pretrained, **kwargs)
docstring: ShuffleNetV2 with 0.5x output channels, as described in
      `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_ 。
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
  
      Examples:
          .. code-block:: python
  
              import paddle
              from paddle.vision.models import shufflenet_v2_x0_5
  
              # build model
              model = shufflenet_v2_x0_5()
  
              # build model and load imagenet pretrained weight
              # model = shufflenet_v2_x0_5(pretrained=True)
  
              x = paddle.rand([1, 3, 224, 224])
              out = model(x)
  
              print(out.shape)
docstring_summary: ShuffleNetV2 with 0.5x output channels, as described in
      `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_ 。
docstring_tokens: ShuffleNetV2 with 0 . 5x output channels as described in ShuffleNet V2 : Practical Guidelines for Ecient CNN Architecture Design <https : // arxiv . org / pdf / 1807 . 11164 . pdf > _ 。
function: def shufflenet_v2_x0_5(pretrained=False, **kwargs):
      """ShuffleNetV2 with 0.5x output channels, as described in
      `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_ 。
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
  
      Examples:
          .. code-block:: python
  
              import paddle
              from paddle.vision.models import shufflenet_v2_x0_5
  
              # build model
              model = shufflenet_v2_x0_5()
  
              # build model and load imagenet pretrained weight
              # model = shufflenet_v2_x0_5(pretrained=True)
  
              x = paddle.rand([1, 3, 224, 224])
              out = model(x)
  
              print(out.shape)
  
      """
      return _shufflenet_v2(
          "shufflenet_v2_x0_5", scale=0.5, pretrained=pretrained, **kwargs)
function_tokens: def shufflenet_v2_x0_5 ( pretrained = False , * * kwargs ) : return _shufflenet_v2 ( "shufflenet_v2_x0_5" , scale = 0.5 , pretrained = pretrained , * * kwargs )