code: def resnext152_64x4d(pretrained=False, **kwargs):
      """ResNeXt-152 64x4d 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-152 64x4d model.
      Examples:
          .. code-block:: python
              import paddle
              from paddle.vision.models import resnext152_64x4d
              # build model
              model = resnext152_64x4d()
              # build model and load imagenet pretrained weight
              # model = resnext152_64x4d(pretrained=True)
              x = paddle.rand([1, 3, 224, 224])
              out = model(x)
              print(out.shape)
              # [1, 1000]
      """
      kwargs["groups"] = 64
      kwargs["width"] = 4
      return _resnet(
          "resnext152_64x4d", BottleneckBlock, 152, pretrained, **kwargs
      )
  
imports: from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  from paddle.utils.download import get_weights_path_from_url
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 12 for calling a global function with example usage: resnext152_64x4d(pretrained) and returns: _resnet