class_docstr: Base class for :py:class:`Estimator`s that wrap Java/Scala
  implementations.
class_docstr_tok: ["Base","class","for",":","py",":","class",":","`","Estimator","`","s","that","wrap","Java\/Scala","implementations","."]
docstr: Fits a Java model to the input dataset.
  
  Examples
  --------
  dataset : :py:class:`pyspark.sql.DataFrame`
      input dataset
  
  Returns
  -------
  py4j.java_gateway.JavaObject
      fitted Java model
docstr_tok: ["Fits","a","Java","model","to","the","input","dataset",".","Examples","--","--","--","--","dataset",":",":","py",":","class",":","`","pyspark.sql.DataFrame","`","input","dataset","Returns","--","--","--","-","py4j.java_gateway.JavaObject","fitted","Java","model"]
code: def _fit_java(self, dataset: DataFrame) -> "JavaObject":
      """
      Fits a Java model to the input dataset.
  
      Examples
      --------
      dataset : :py:class:`pyspark.sql.DataFrame`
          input dataset
  
      Returns
      -------
      py4j.java_gateway.JavaObject
          fitted Java model
      """
      assert self._java_obj is not None
  
      self._transfer_params_to_java()
      return self._java_obj.fit(dataset._jdf)
  
imports: from abc import ABCMeta, abstractmethod
  from typing import Any, Generic, Optional, List, Type, TypeVar, TYPE_CHECKING
  from pyspark import since
  from pyspark import SparkContext
  from pyspark.sql import DataFrame
  from pyspark.ml import Estimator, Predictor, PredictionModel, Transformer, Model
  from pyspark.ml.base import _PredictorParams
  from pyspark.ml.param import Param, Params
  from pyspark.ml.util import _jvm
  from pyspark.ml.common import inherit_doc, _java2py, _py2java
label_desc: Use image node_id 2 for calling the JavaEstimator obj's underlying member method code with example usage: obj._fit_java(dataset) and returns: self