class_docstr: Args:
  
      inputCol (str): The name of the input column (default: )
      outputCol (str): The name of the output column
      udf (object): User Defined Python Function to be applied to the DF input col
      udfScala (object): User Defined Function to be applied to the DF input col
class_docstr_tok: ["Args",":","inputCol","(","str",")",":","The","name","of","the","input","column","(","default",":",")","outputCol","(","str",")",":","The","name","of","the","output","column","udf","(","object",")",":","User","Defined","Python","Function","to","be","applied","to","the","DF","input","col","udfScala","(","object",")",":","User","Defined","Function","to","be","applied","to","the","DF","input","col"]
code: def __init__(
      self, inputCol=None, inputCols=None, outputCol=None, udf=None
  ):
      super(UDFTransformer, self).__init__()
      self._java_obj = self._new_java_obj(
          "com.microsoft.azure.synapse.ml.stages.UDFTransformer",
      )
      self.inputCol = Param(
          self,
          "inputCol",
          "inputCol: The name of the input column (default: )",
      )
      self.inputCols = Param(
          self,
          "inputCols",
          "inputCols: The names of the input columns (default: )",
      )
      self.outputCol = Param(
          self,
          "outputCol",
          "outputCol: The name of the output column",
      )
      self.udf = Param(
          self,
          "udf",
          "udf: User Defined Python Function to be applied to the DF input col",
      )
      if udf != None:
          self.setUDF(udf)
          self._udf = udf
      if inputCol != None:
          self.setInputCol(inputCol)
      if inputCols != None:
          self.setInputCols(inputCols)
      if outputCol != None:
          self.setOutputCol(outputCol)
  
imports: import sys
  from pyspark.ml.param.shared import *
  from synapse.ml.stages._UDFTransformer import _UDFTransformer
  from pyspark import keyword_only
  from pyspark.ml.util import JavaMLReadable, JavaMLWritable
  from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
  from pyspark.ml.common import inherit_doc
  from pyspark.sql.functions import UserDefinedFunction
  from pyspark.ml.common import inherit_doc
  from synapse.ml.core.schema.Utils import *
label_desc: Use image node_id 1 to create a new UDFTransformer object from inherited base classes: ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer with example: obj = UDFTransformer(inputCol, inputCols, outputCol, udf)