text: from .epoch_metric import EpochMetric
  
  def roc_auc_compute_fn(y_preds, y_targets):
      try:
          from sklearn.metrics import roc_auc_score
      except ImportError:
          raise RuntimeError("This contrib module requires sklearn to be installed.")
  
      y_true = y_targets.numpy()
      y_pred = y_preds.numpy()
      return roc_auc_score(y_true, y_pred)
  
  
  class ROC_AUC(EpochMetric):
      """Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC)
      accumulating predictions and the ground-truth during an epoch and applying
      `sklearn.metrics.roc_auc_score <http://scikit-learn.org/stable/modules/generated/
      sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_ .
      Args:
          output_transform (callable, optional): a callable that is used to transform the
              :class:`~ignite.engine.Engine`'s `process_function`'s output into the
              form expected by the metric. This can be useful if, for example, you have a multi-output model and
              you want to compute the metric with respect to one of the outputs.
      ROC_AUC expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence
      values. To apply an activation to y_pred, use output_transform as shown below:
      .. code-block:: python
          def activated_output_transform(output):
              y_pred, y = output
              y_pred = torch.sigmoid(y_pred)
              return y_pred, y
          roc_auc = ROC_AUC(activated_output_transform)
      """
  
      def __init__(self, output_transform=lambda x: x):
          super(ROC_AUC, self).__init__(roc_auc_compute_fn, output_transform=output_transform)