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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docstr: Partially fit underlying estimators
  
  Should be used when memory is inefficient to train all data. Chunks
  of data can be passed in several iteration, where the first call
  should have an array of all target variables.
  
  
  Parameters
  ----------
  X : (sparse) array-like, shape = [n_samples, n_features]
      Data.
  
  y : array-like, shape = [n_samples]
      Multi-class targets.
  
  classes : array, shape (n_classes, )
      Classes across all calls to partial_fit.
      Can be obtained via `np.unique(y_all)`, where y_all is the
      target vector of the entire dataset.
      This argument is only required in the first call of partial_fit
      and can be omitted in the subsequent calls.
  
  Returns
  -------
  self
code: def partial_fit(self, X, y, classes=None):
      """Partially fit underlying estimators
  
      Should be used when memory is inefficient to train all data. Chunks
      of data can be passed in several iteration, where the first call
      should have an array of all target variables.
  
  
      Parameters
      ----------
      X : (sparse) array-like, shape = [n_samples, n_features]
          Data.
  
      y : array-like, shape = [n_samples]
          Multi-class targets.
  
      classes : array, shape (n_classes, )
          Classes across all calls to partial_fit.
          Can be obtained via `np.unique(y_all)`, where y_all is the
          target vector of the entire dataset.
          This argument is only required in the first call of partial_fit
          and can be omitted in the subsequent calls.
  
      Returns
      -------
      self
      """
      if _check_partial_fit_first_call(self, classes):
          self.estimators_ = [
              clone(self.estimator)
              for i in range(
                  self.n_classes_ * (self.n_classes_ - 1) // 2
              )
          ]
  
      X, y = check_X_y(X, y, accept_sparse=["csr", "csc"])
      check_classification_targets(y)
      combinations = itertools.combinations(range(self.n_classes_), 2)
      self.estimators_ = Parallel(n_jobs=self.n_jobs)(
          delayed(_partial_fit_ovo_binary)(
              estimator, X, y, self.classes_[i], self.classes_[j]
          )
          for estimator, (i, j) in izip(
              self.estimators_, (combinations)
          )
      )
  
      self.pairwise_indices_ = None
  
      return self
  
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 3 for calling the OneVsOneClassifier obj's underlying member method code with example usage: obj.partial_fit(X, y, classes) and returns: self