class_docstr: Base class for pairs learners.
  
  Attributes
  ----------
  threshold_ : `float`
    If the distance metric between two points is lower than this threshold,
    points will be classified as similar, otherwise they will be
    classified as dissimilar.
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docstr: Computes score of pairs similarity prediction.
  
  Returns the ``roc_auc`` score of the fitted metric learner. It is
  computed in the following way: for every value of a threshold
  ``t`` we classify all pairs of samples where the predicted distance is
  inferior to ``t`` as belonging to the "similar" class, and the other as
  belonging to the "dissimilar" class, and we count false positive and
  true positives as in a classical ``roc_auc`` curve.
  
  Parameters
  ----------
  pairs : array-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)
    3D Array of pairs, with each row corresponding to two points,
    or 2D array of indices of pairs if the metric learner uses a
    preprocessor.
  
  y : array-like, shape=(n_constraints,)
    The corresponding labels.
  
  Returns
  -------
  score : float
    The ``roc_auc`` score.
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code: def score(self, pairs, y):
      """Computes score of pairs similarity prediction.
  
      Returns the ``roc_auc`` score of the fitted metric learner. It is
      computed in the following way: for every value of a threshold
      ``t`` we classify all pairs of samples where the predicted distance is
      inferior to ``t`` as belonging to the "similar" class, and the other as
      belonging to the "dissimilar" class, and we count false positive and
      true positives as in a classical ``roc_auc`` curve.
  
      Parameters
      ----------
      pairs : array-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2)
        3D Array of pairs, with each row corresponding to two points,
        or 2D array of indices of pairs if the metric learner uses a
        preprocessor.
  
      y : array-like, shape=(n_constraints,)
        The corresponding labels.
  
      Returns
      -------
      score : float
        The ``roc_auc`` score.
      """
      return roc_auc_score(y, self.decision_function(pairs))
  
imports: from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.extmath import stable_cumsum
  from sklearn.utils.validation import _is_arraylike, check_is_fitted
  from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve
  import numpy
  from abc import ABCMeta, abstractmethod
  from ._util import ArrayIndexer, check_input, validate_vector
  import warnings
label_desc: Use image node_id 3 for calling the _PairsClassifierMixin obj's underlying member method code with example usage: obj.score(pairs, y) and returns: roc_auc_score