code: def roc_auc_score(
      y_true: Union[List[List[float]], List[List[int]], np.ndarray],
      y_pred: Union[List[List[float]], List[List[int]], np.ndarray],
  ) -> float:
      """
      Compute Area Under the Curve (AUC) from prediction scores.
  
      Args:
          y_true: true binary labels
          y_pred: target scores, can either be probability estimates of the positive class
  
      Returns:
          Area Under the Curve (AUC) from prediction scores
  
      Alias:
          roc_auc
      """
      try:
          return sklearn.metrics.roc_auc_score(
              np.squeeze(np.array(y_true)),
              np.squeeze(np.array(y_pred)),
              average="macro",
          )
      except ValueError:
          return 0.0
  
imports: from typing import List, Union
  import numpy
  import sklearn.metrics
  from deeppavlov.core.common.metrics_registry import register_metric
label_desc: Use image node_id 1 for calling a global function with example usage: roc_auc_score(y_true, y_pred) and returns: sklearn, int