class_docstr: Same as sklearn `OneVsOneClassifier` but with distributed
  training using spark.
  
  Args:
      estimator (sklearn estimator): An estimator object implementing fit and one of
          decision_function or predict_proba.
      sc (sparkContext): Spark context for spark broadcasting and rdd operations.
      partitions (int or 'auto'): default 'auto'
          Number of partitions to use for parallelization of parameter
          search space. Integer values or None will be used directly for `numSlices`,
          while 'auto' will set `numSlices` to the number required fits.
      verbose (bool): print status messages
      **kwargs: Keyword arguments to be passed to `OneVsOneClassifier`.
class_docstr_tok: ["Same","as","sklearn","`","OneVsOneClassifier","`","but","with","distributed","training","using","spark",".","Args",":","estimator","(","sklearn","estimator",")",":","An","estimator","object","implementing","fit","and","one","of","decision_function","or","predict_proba",".","sc","(","sparkContext",")",":","Spark","context","for","spark","broadcasting","and","rdd","operations",".","partitions","(","int","or","'auto","'",")",":","default","'auto'","Number","of","partitions","to","use","for","parallelization","of","parameter","search","space",".","Integer","values","or","None","will","be","used","directly","for","`","numSlices","`",",","while","'auto","'","will","set","`","numSlices","`","to","the","number","required","fits",".","verbose","(","bool",")",":","print","status","messages","*","*","kwargs",":","Keyword","arguments","to","be","passed","to","`","OneVsOneClassifier","`","."]
code: def __init__(
      self,
      estimator,
      sc=None,
      partitions="auto",
      verbose=False,
      **kwargs,
  ):
      OneVsOneClassifier.__init__(self, estimator, **kwargs)
      self.sc = sc
      self.partitions = partitions
      self.verbose = verbose
  
imports: import numpy
  import pandas
  import warnings
  from joblib import Parallel, delayed
  from copy import copy
  from collections.abc import Sequence
  from scipy.sparse import vstack
  from sklearn.base import BaseEstimator
  from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier
  from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
  from sklearn.preprocessing import normalize
  from sklearn.utils import shuffle
  from sklearn.utils.validation import check_X_y
  from sklearn.utils.multiclass import check_classification_targets, type_of_target
  from sklearn.model_selection import train_test_split, GroupKFold
  from .validation import _check_estimator
  from .utils import _safe_split
  from .base import _clone, _get_value, _parse_partitions
label_desc: Use image node_id 1 to create a new DistOneVsOneClassifier object from inherited base classes: OneVsOneClassifier with example: obj = DistOneVsOneClassifier(estimator, sc, partitions, verbose)