class_docstr: Same as sklearn `BaseForest` but with distributed
  training using spark.
  
  Args:
      base_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.
      **kwargs: Keyword arguments to be passed to `BaseForest`.
class_docstr_tok: ["Same","as","sklearn","`","BaseForest","`","but","with","distributed","training","using","spark",".","Args",":","base_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",".","*","*","kwargs",":","Keyword","arguments","to","be","passed","to","`","BaseForest","`","."]
code: def __init__(
      self, base_estimator, sc=None, partitions="auto", **kwargs
  ):
      BaseForest.__init__(self, base_estimator, **kwargs)
      self.sc = sc
      self.partitions = partitions
      for key, val in kwargs.items():
          setattr(self, key, val)
  
imports: import numpy
  import pandas
  from joblib import Parallel, delayed
  from scipy.sparse import hstack, issparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.ensemble.forest import BaseForest, ExtraTreesClassifier, ExtraTreesRegressor, ForestClassifier, ForestRegressor, RandomForestClassifier, RandomForestRegressor
  from sklearn.tree import ExtraTreeRegressor
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.model_selection import KFold
  from sklearn.utils import check_array, check_random_state
  from numpy import float32
  from numpy import float64
  from .validation import _check_estimator, _check_is_fitted
  from .base import _clone, _get_value, _parse_partitions
label_desc: Use image node_id 1 to create a new DistBaseForest object from inherited base classes: BaseForest with example: obj = DistBaseForest(base_estimator, sc, partitions)