code: def wide_resnet50_2(pretrained=False, **kwargs):
      """Wide ResNet-50-2 model from
      `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
  
      Examples:
          .. code-block:: python
  
              import paddle
              from paddle.vision.models import wide_resnet50_2
  
              # build model
              model = wide_resnet50_2()
  
              # build model and load imagenet pretrained weight
              # model = wide_resnet50_2(pretrained=True)
  
              x = paddle.rand([1, 3, 224, 224])
              out = model(x)
  
              print(out.shape)
      """
      kwargs["width"] = 64 * 2
      return _resnet(
          "wide_resnet50_2", BottleneckBlock, 50, pretrained, **kwargs
      )
  
imports: from __future__ import print_function
  import paddle
  import paddle.nn
  from paddle.utils.download import get_weights_path_from_url
label_desc: Use image node_id 7 for calling a global function with example usage: wide_resnet50_2(pretrained) and returns: _resnet