def resnext50_32x4d(pretrained=False, **kwargs):
    """ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
    
    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 32x4d model.
    Examples:
        .. code-block:: python
            import paddle
            from paddle.vision.models import resnext50_32x4d
            # build model
            model = resnext50_32x4d()
            # build model and load imagenet pretrained weight
            # model = resnext50_32x4d(pretrained=True)
            x = paddle.rand([1, 3, 224, 224])
            out = model(x)
            print(out.shape)
            # [1, 1000]
    """
    kwargs['groups'] = 32
    kwargs['width'] = 4
    return _resnet('resnext50_32x4d', BottleneckBlock, 50, pretrained, **kwargs)
ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
    
    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
                            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_ResNet>`.
    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 32x4d model.
    Examples:
        .. code-block:: python
            import paddle
            from paddle.vision.models import resnext50_32x4d
            # build model
            model = resnext50_32x4d()
            # build model and load imagenet pretrained weight
            # model = resnext50_32x4d(pretrained=True)
            x = paddle.rand([1, 3, 224, 224])
            out = model(x)
            print(out.shape)
            # [1, 1000]
    
ppdet/modeling/backbones/clrnet_resnet.py