class_docstr: Same as sklearn `OneVsRestClassifier` but with distributed
  training using spark. Additionally implements flexible
  ``predict_proba`` method with custom `norm` input
  designating the normalization method used after individual
  predictions are made.
  
  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.
      norm (string): default None, Normalization method for predict_proba.
      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.
      max_negatives (int or float): default None
          Maximum number of negative records allowed for each binary
          estimator. Use int for hard maximum, or float for percentage
          of total negatives.
      random_state (int): default None
          Random state for limiting negatives (if max_negatives is not None).
      method (str): 'ratio' or 'multiplier'
          Method used to calculate true maximum number of negatives.
      n_splits (int): default 1
          Dials the number of splits for broadcasting
          X during fitting. Use values higher than 1 for large X.
      mlb_override (bool): pass over mlb step; this assumes
          that input `y` to `fit` is already in sparse (one-hot-encoded)
          format
      verbose (bool): print status messages
      **kwargs: Keyword arguments to be passed to `OneVsRestClassifier`.
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docstr: Probability estimates.
  The returned estimates for all classes are ordered by label of classes.
  
  Args:
      X (array-like, shape = [n_samples, n_features]): input data
  
  Returns:
      T (array-like, shape = [n_samples, n_classes]): returns the probability
          of the sample for each class in the model, where classes are
          ordered as they are in self.classes_
docstr_tok: ["Probability","estimates",".","The","returned","estimates","for","all","classes","are","ordered","by","label","of","classes",".","Args",":","X","(","array-like",",","shape","=","[","n_samples",",","n_features","]",")",":","input","data","Returns",":","T","(","array-like",",","shape","=","[","n_samples",",","n_classes","]",")",":","returns","the","probability","of","the","sample","for","each","class","in","the","model",",","where","classes","are","ordered","as","they","are","in","self.classes_"]
code: def predict_proba(self, X):
      """
      Probability estimates.
      The returned estimates for all classes are ordered by label of classes.
  
      Args:
          X (array-like, shape = [n_samples, n_features]): input data
  
      Returns:
          T (array-like, shape = [n_samples, n_classes]): returns the probability
              of the sample for each class in the model, where classes are
              ordered as they are in self.classes_
      """
      probs = []
      for index in range(len(self.estimators_)):
          probs.append(self.estimators_[index].predict_proba(X)[:, 1])
      out = np.array(
          [
              [probs[y][index] for y in range(len(self.estimators_))]
              for index in range(len(probs[0]))
          ]
      )
      if self.norm:
          return normalize(out, norm=self.norm)
      else:
          return out
  
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 4 for calling the DistOneVsRestClassifier obj's underlying member method code with example usage: obj.predict_proba(X) and returns: normalize, out