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
      An estimator object implementing `fit` and one of `decision_function`
      or `predict_proba`.
  
  n_jobs : int, optional, default: 1
      The number of jobs to use for the computation. If -1 all CPUs are used.
      If 1 is given, no parallel computing code is used at all, which is
      useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
      used. Thus for n_jobs = -2, all CPUs but one are used.
  
  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.
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code: def n_classes_(self):
      return len(self.classes_)
  
imports: import array
  import numpy
  import warnings
  import scipy.sparse
  import itertools
  from .base import BaseEstimator, ClassifierMixin, clone, is_classifier
  from .base import MetaEstimatorMixin, is_regressor
  from .preprocessing import LabelBinarizer
  from .metrics.pairwise import euclidean_distances
  from .utils import check_random_state
  from .utils.validation import _num_samples
  from .utils.validation import check_is_fitted
  from .utils.validation import check_X_y
  from .utils.multiclass import _check_partial_fit_first_call, check_classification_targets, _ovr_decision_function
  from .utils.metaestimators import _safe_split
  from .externals.joblib import Parallel
  from .externals.joblib import delayed
  from .externals.six.moves import zip
label_desc: Use image node_id 6 for calling the OneVsOneClassifier obj's underlying member method code with example usage: obj.n_classes_() and returns: len