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 a Java ParamMap into a Python ParamMap.
docstr_tok: ["Transforms","a","Java","ParamMap","into","a","Python","ParamMap","."]
code: def _transfer_param_map_from_java(
      self, javaParamMap: "JavaObject"
  ) -> "ParamMap":
      """
      Transforms a Java ParamMap into a Python ParamMap.
      """
      sc = SparkContext._active_spark_context
      assert sc is not None
  
      paramMap = dict()
      for pair in javaParamMap.toList():
          param = pair.param()
          if self.hasParam(str(param.name())):
              paramMap[self.getParam(param.name())] = _java2py(
                  sc, pair.value()
              )
      return paramMap
  
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 6 for calling the JavaParams obj's underlying member method code with example usage: obj._transfer_param_map_from_java(javaParamMap) and returns: paramMap