class_docstr: Utility class to help create wrapper classes from Java/Scala
  implementations of pipeline components.
class_docstr_tok: ["Utility","class","to","help","create","wrapper","classes","from","Java\/Scala","implementations","of","pipeline","components","."]
docstr: Transforms the embedded params to the companion Java object.
docstr_tok: ["Transforms","the","embedded","params","to","the","companion","Java","object","."]
code: def _transfer_params_to_java(self) -> None:
      """
      Transforms the embedded params to the companion Java object.
      """
      assert self._java_obj is not None
  
      pair_defaults = []
      for param in self.params:
          if self.isSet(param):
              pair = self._make_java_param_pair(
                  param, self._paramMap[param]
              )
              self._java_obj.set(pair)
          if self.hasDefault(param):
              pair = self._make_java_param_pair(
                  param, self._defaultParamMap[param]
              )
              pair_defaults.append(pair)
      if len(pair_defaults) > 0:
          sc = SparkContext._active_spark_context
          assert sc is not None and sc._jvm is not None
  
          pair_defaults_seq = sc._jvm.PythonUtils.toSeq(pair_defaults)
          self._java_obj.setDefault(pair_defaults_seq)
  
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 JavaParams obj's underlying member method code with example usage: obj._transfer_params_to_java() without return types