docstr: Ensure the provided column list is a list of strings.
  
  :param column_purpose: The purpose the column fulfills in the dataset.
  :type column_purpose: str
  :param column_names: Column names to be validate.
  :type column_names: Any
docstr_tok: ["Ensure","the","provided","column","list","is","a","list","of","strings",".",":","param","column_purpose",":","The","purpose","the","column","fulfills","in","the","dataset",".",":","type","column_purpose",":","str",":","param","column_names",":","Column","names","to","be","validate",".",":","type","column_names",":","Any"]
code: def _validate_column_list(column_purpose: str, column_names: Any):
      """Ensure the provided column list is a list of strings.
  
      :param column_purpose: The purpose the column fulfills in the dataset.
      :type column_purpose: str
      :param column_names: Column names to be validate.
      :type column_names: Any
      """
      if column_names is None:
          return
      if column_names is not None and not isinstance(
          column_names, list
      ):
          raise UserConfigValidationException(
              "{} must be a list.".format(column_purpose)
          )
  
      for column_name in column_names:
          if not isinstance(column_name, str):
              raise UserConfigValidationException(
                  "Entries in {} must be strings.".format(
                      column_purpose
                  )
              )
  
file_path: responsible-ai-toolbox/responsibleai/responsibleai/feature_metadata.py
imports: from typing import Any, Dict, List, Optional
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 5 for calling the FeatureMetadata obj's underlying member method code with example usage: obj._validate_column_list(column_purpose, column_names) without return types