class_docstr: Provides arguments to Spark UDFs.
docstr: Determine if a function has a parameter with a given name.
  
  Args:
      udf (Callable): the user defined function.
      name (str): the name of the parameter.
  
  Returns:
      bool: True if the udf contains a parameter with the name.
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code: def _has_param(self, udf: Callable, name: str) -> bool:
      """Determine if a function has a parameter with a given name.
  
      Args:
          udf (Callable): the user defined function.
          name (str): the name of the parameter.
  
      Returns:
          bool: True if the udf contains a parameter with the name.
      """
      return name in list(signature(udf).parameters.keys())
  
imports: from __future__ import absolute_import
  from abc import ABC, abstractmethod
  from inspect import signature
  from typing import Any, Callable, Dict, Generic, List, OrderedDict, TypeVar, Union, Optional
  import attr
  from pyspark.sql import DataFrame, SparkSession
  from sagemaker.feature_store.feature_processor._data_source import CSVDataSource, FeatureGroupDataSource, ParquetDataSource, BaseDataSource, PySparkDataSource
  from sagemaker.feature_store.feature_processor._feature_processor_config import FeatureProcessorConfig
  from sagemaker.feature_store.feature_processor._input_loader import SparkDataFrameInputLoader
  from sagemaker.feature_store.feature_processor._params_loader import ParamsLoader
  from sagemaker.feature_store.feature_processor._spark_factory import SparkSessionFactory
label_desc: Use image node_id 6 for calling the SparkArgProvider obj's underlying member method code with example usage: obj._has_param(udf, name) and returns: unknown