code: def roc_auc_score(y_true, y_score):
      """
      Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
      from prediction scores.
  
      .. note:: this implementation can only be used with binary classification.
  
      Parameters
      ----------
      y_true : array-like of shape (n_samples,)
          True labels. The binary cases
          expect labels with shape (n_samples,)
      y_score : array-like of shape (n_samples,)
          Target scores. In the binary cases, these can be either
          probability estimates or non-thresholded decision values (as returned
          by `decision_function` on some classifiers). The binary
          case expects a shape (n_samples,), and the scores must be the scores of
          the class with the greater label.
  
      Returns
      -------
          auc : float
  
      Examples
      --------
      >>> import numpy as np
      >>> from cuml.metrics import roc_auc_score
      >>> y_true = np.array([0, 0, 1, 1])
      >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
      >>> print(roc_auc_score(y_true, y_scores))
      0.75
  
      """
      y_true, n_rows, n_cols, ytype = input_to_cupy_array(
          y_true,
          check_dtype=[np.int32, np.int64, np.float32, np.float64],
      )
  
      y_score, _, _, _ = input_to_cupy_array(
          y_score,
          check_dtype=[np.int32, np.int64, np.float32, np.float64],
          check_rows=n_rows,
          check_cols=n_cols,
      )
      return _binary_roc_auc_score(y_true, y_score)
  
file_path: cuml/python/cuml/metrics/_ranking.py
imports: import math
  from cuml.internals.input_utils import input_to_cupy_array
  from cuml.internals.array import CumlArray
  import cuml.internals
  from cuml.internals.safe_imports import cpu_only_import
  import typing
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 2 for calling a global function with example usage: roc_auc_score(y_true, y_score) and returns: _binary_roc_auc_score