class_docstr: Base class for :py:class:`Transformer`s that wrap Java/Scala
  implementations. Subclasses should ensure they have the transformer Java object
  available as _java_obj.
class_docstr_tok: ["Base","class","for",":","py",":","class",":","`","Transformer","`","s","that","wrap","Java\/Scala","implementations",".","Subclasses","should","ensure","they","have","the","transformer","Java","object","available","as","_java_obj","."]
code: def _transform(self, dataset: DataFrame) -> DataFrame:
      assert self._java_obj is not None
  
      self._transfer_params_to_java()
      return DataFrame(
          self._java_obj.transform(dataset._jdf), dataset.sparkSession
      )
  
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 1 for calling the JavaTransformer obj's underlying member method code with example usage: obj._transform(dataset) and returns: DataFrame