code: def get_fourier_feature_col_names(
      df: pd.DataFrame,
      time_col: str,
      fs_func: callable,
      conti_year_origin: Optional[int] = None,
  ) -> List[str]:
      """Gets the Fourier feature column names.
  
      Parameters
      ----------
      df : `pandas.DataFrame`
          The input data.
      time_col : `str`
          The column name for timestamps in ``df``.
      fs_func : callable
          The function to generate Fourier features.
      conti_year_origin : `int` or None, default None
          The continuous year origin.
          If None, will be inferred from time column.
          The names do not depend on this parameter though.
  
      Returns
      -------
      col_names : `list` [`str`]
          The list of Fourier feature column names.
      """
      if conti_year_origin is None:
          conti_year_origin = get_default_origin_for_time_vars(
              df=df, time_col=time_col
          )
      time_features_example_df = build_time_features_df(
          df[time_col][:2], conti_year_origin=conti_year_origin
      )
      fs = fs_func(time_features_example_df)
      return fs["cols"]
  
file_path: greykite/greykite/algo/forecast/silverkite/forecast_silverkite_helper.py
imports: import math
  import warnings
  from typing import List
  from typing import Optional
  import pandas
  from greykite.common.constants import TimeFeaturesEnum
  from greykite.common.enums import SimpleTimeFrequencyEnum
  from greykite.common.features.timeseries_features import build_time_features_df
  from greykite.common.features.timeseries_features import get_default_origin_for_time_vars
label_desc: Use image node_id 4 for calling a global function with example usage: get_fourier_feature_col_names(df, time_col, fs_func, conti_year_origin) and returns: fs