parameters: (pretrained=False, **kwargs)
return_statement: return _resnext('resnext50_64x4d', 50, 64, pretrained, **kwargs)
docstring: ResNeXt-50 64x4d model from
      `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
      
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
  
      Examples:
          .. code-block:: python
  
              import paddle
              from paddle.vision.models import resnext50_64x4d
  
              # build model
              model = resnext50_64x4d()
  
              # build model and load imagenet pretrained weight
              # model = resnext50_64x4d(pretrained=True)
docstring_summary: ResNeXt-50 64x4d model from
      `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
      
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
docstring_tokens: ResNeXt - 50 64x4d model from Aggregated Residual Transformations for Deep Neural Networks <https : // arxiv . org / pdf / 1611 . 05431 . pdf > _ Args : pretrained ( bool ) : If True returns a model pre - trained on ImageNet
function: def resnext50_64x4d(pretrained=False, **kwargs):
      """ResNeXt-50 64x4d model from
      `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
      
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
  
      Examples:
          .. code-block:: python
  
              import paddle
              from paddle.vision.models import resnext50_64x4d
  
              # build model
              model = resnext50_64x4d()
  
              # build model and load imagenet pretrained weight
              # model = resnext50_64x4d(pretrained=True)
      """
      return _resnext('resnext50_64x4d', 50, 64, pretrained, **kwargs)
function_tokens: def resnext50_64x4d ( pretrained = False , * * kwargs ) : return _resnext ( 'resnext50_64x4d' , 50 , 64 , pretrained , * * kwargs )