code: def _parse_generic_column(
      df: pd.DataFrame, column_or_value: Any
  ) -> Tuple[str | None, Any]:
      if (
          isinstance(column_or_value, str)
          and column_or_value in df.columns
      ):
          column_name = column_or_value
          value = None
      else:
          column_name = None
          value = column_or_value
  
      return column_name, value
  
imports: from __future__ import annotations
  from contextlib import nullcontext
  from datetime import date
  from enum import Enum
  from typing import TYPE_CHECKING, Any, Collection, Dict, List, Sequence, Tuple, cast
  import pandas
  from pandas.api.types import infer_dtype, is_integer_dtype
  from typing_extensions import Literal
  import streamlit.elements.arrow_vega_lite
  from streamlit import type_util
  from streamlit.color_util import Color, is_color_like, is_color_tuple_like, is_hex_color_like, to_css_color
  from streamlit.elements.altair_utils import AddRowsMetadata
  from streamlit.elements.arrow import Data
  from streamlit.elements.utils import last_index_for_melted_dataframes
  from streamlit.errors import Error, StreamlitAPIException
  from streamlit.proto.ArrowVegaLiteChart_pb2 import ArrowVegaLiteChart
  from streamlit.runtime.metrics_util import gather_metrics
label_desc: Use image node_id 9 for calling a global function with example usage: _parse_generic_column(df, column_or_value) and returns: column_name,  value