code: def roc_auc_score(stc_true, stc_est, per_sample=True):
      """Compute ROC AUC between 2 source estimates.
  
      ROC stands for receiver operating curve and AUC is Area under the curve.
      When computing this metric the stc_true must be thresholded
      as any non-zero value will be considered as a positive.
  
      The ROC-AUC metric is computed between amplitudes of the source
      estimates, i.e. after taking the absolute values.
  
      Parameters
      ----------
      %(stc_true_metric)s
      %(stc_est_metric)s
      %(per_sample_metric)s
  
      Returns
      -------
      %(stc_metric)s
  
      Notes
      -----
      .. versionadded:: 1.2
      """
      stc_true, stc_est = _uniform_stc(stc_true, stc_est)
      metric = _apply(
          _roc_auc_score, stc_true, stc_est, per_sample=per_sample
      )
      return metric
  
imports: from functools import partial
  import numpy
  from scipy.spatial.distance import cdist
  from ...utils import _check_option, _validate_type, fill_doc
label_desc: Use image node_id 13 for calling a global function with example usage: roc_auc_score(stc_true, stc_est, per_sample) and returns: metric