class_docstr: One-vs-one multiclass strategy.
  
  This strategy consists in fitting one classifier per class pair.
  At prediction time, the class which received the most votes is selected.
  Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers,
  this method is usually slower than one-vs-the-rest, due to its
  O(n_classes^2) complexity. However, this method may be advantageous for
  algorithms such as kernel algorithms which don't scale well with
  `n_samples`. This is because each individual learning problem only involves
  a small subset of the data whereas, with one-vs-the-rest, the complete
  dataset is used `n_classes` times.
  
  Read more in the :ref:`User Guide <ovo_classification>`.
  
  Parameters
  ----------
  estimator : estimator object
      A regressor or a classifier that implements :term:`fit`.
      When a classifier is passed, :term:`decision_function` will be used
      in priority and it will fallback to :term:`predict_proba` if it is not
      available.
      When a regressor is passed, :term:`predict` is used.
  
  n_jobs : int, default=None
      The number of jobs to use for the computation: the `n_classes * (
      n_classes - 1) / 2` OVO problems are computed in parallel.
  
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  Attributes
  ----------
  estimators_ : list of ``n_classes * (n_classes - 1) / 2`` estimators
      Estimators used for predictions.
  
  classes_ : numpy array of shape [n_classes]
      Array containing labels.
  
  n_classes_ : int
      Number of classes.
  
  pairwise_indices_ : list, length = ``len(estimators_)``, or ``None``
      Indices of samples used when training the estimators.
      ``None`` when ``estimator``'s `pairwise` tag is False.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  OneVsRestClassifier : One-vs-all multiclass strategy.
  OutputCodeClassifier : (Error-Correcting) Output-Code multiclass strategy.
  
  Examples
  --------
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.model_selection import train_test_split
  >>> from sklearn.multiclass import OneVsOneClassifier
  >>> from sklearn.svm import LinearSVC
  >>> X, y = load_iris(return_X_y=True)
  >>> X_train, X_test, y_train, y_test = train_test_split(
  ...     X, y, test_size=0.33, shuffle=True, random_state=0)
  >>> clf = OneVsOneClassifier(
  ...     LinearSVC(dual="auto", random_state=0)).fit(X_train, y_train)
  >>> clf.predict(X_test[:10])
  array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1])
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docstr: Indicate if wrapped estimator is using a precomputed Gram matrix
code: def _more_tags(self):
      """Indicate if wrapped estimator is using a precomputed Gram matrix"""
      return {"pairwise": _safe_tags(self.estimator, key="pairwise")}
  
imports: import array
  import itertools
  import warnings
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, MultiOutputMixin, _fit_context, clone, is_classifier, is_regressor
  from .metrics.pairwise import pairwise_distances_argmin
  from .preprocessing import LabelBinarizer
  from .utils import check_random_state
  from .utils._param_validation import HasMethods, Interval
  from .utils._tags import _safe_tags
  from .utils.metaestimators import _safe_split, available_if
  from .utils.multiclass import _check_partial_fit_first_call, _ovr_decision_function, check_classification_targets
  from .utils.parallel import Parallel, delayed
  from .utils.validation import _num_samples, check_is_fitted
label_desc: Use image node_id 7 for calling the OneVsOneClassifier obj's underlying member method code with example usage: obj._more_tags() and returns: dict