class_docstr: Base class for arguments providers for the UDF.
  
  Args:
      Generic (T): The type of the auto-loaded data values.
docstr: Provides the 'params' argument that is provided to the UDF.
  
  Args:
      udf (Callable[..., T]): The feature_processor wrapped user function.
      fp_config (FeatureProcessorConfig): The configuration for the feature_processor.
  
  Returns:
      Dict[str, Dict]: A combination of user defined parameters (in fp_config) and system
          provided parameters.
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code: def provide_params_arg(
      self, udf: Callable[..., T], fp_config: FeatureProcessorConfig
  ) -> Dict[str, Dict]:
      """Provides the 'params' argument that is provided to the UDF.
  
      Args:
          udf (Callable[..., T]): The feature_processor wrapped user function.
          fp_config (FeatureProcessorConfig): The configuration for the feature_processor.
  
      Returns:
          Dict[str, Dict]: A combination of user defined parameters (in fp_config) and system
              provided parameters.
      """
  
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 2 for calling the UDFArgProvider obj's underlying member method code with example usage: obj.provide_params_arg(udf, fp_config) without return types