code: def recall_score(stc_true, stc_est, threshold="90%", per_sample=True):
      """Compute the recall.
  
      The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
      true positives and ``fn`` the number of false negatives. The recall is
      intuitively the ability of the classifier to find all the positive samples.
  
      The best value is 1 and the worst value is 0.
  
      Threshold is used first for data binarization.
  
      Parameters
      ----------
      %(stc_true_metric)s
      %(stc_est_metric)s
      threshold : float | str
          The threshold to apply to source estimates before computing
          the recall. If a string the threshold is
          a percentage and it should end with the percent character.
      %(per_sample_metric)s
  
      Returns
      -------
      %(stc_metric)s
  
      Notes
      -----
      .. versionadded:: 1.2
      """
      stc_true, stc_est = _uniform_stc(stc_true, stc_est)
      stc_true, stc_est = _thresholding(stc_true, stc_est, threshold)
      metric = _apply(
          _recall_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 19 for calling a global function with example usage: recall_score(stc_true, stc_est, threshold, per_sample) and returns: metric