code: def fourier_series(dates, period, series_order):
      """Provides Fourier series components with the specified frequency and order.
      Note
      ----
      Identical to OG Prophet.
      Parameters
      ----------
          dates : pd.Series
              Containing timestamps
          period : float
              Number of days of the period
          series_order : int
              Number of fourier components
      Returns
      -------
          np.array
              Matrix with seasonality features
      """
      # convert to days since epoch
      t = np.array(
          (dates - datetime(1970, 1, 1))
          .dt.total_seconds()
          .astype(np.float32)
      ) / (3600 * 24.0)
      return fourier_series_t(t, period, series_order)
  
imports: import logging
  from collections import OrderedDict, defaultdict
  from datetime import datetime
  from typing import Optional
  import numpy
  import pandas
  import torch
  from torch.utils.data.dataset import Dataset
  from neuralprophet import configure, utils
  from neuralprophet.df_utils import get_max_num_lags
  from neuralprophet.hdays_utils import get_country_holidays
label_desc: Use image node_id 2 for calling a global function with example usage: fourier_series(dates, period, series_order) and returns: fourier_series_t