code: def fourier_series_t(t, period, series_order):
      """Provides Fourier series components with the specified frequency and order.
      Note
      ----
      This function is identical to Meta AI's Prophet Library
      Parameters
      ----------
          t : pd.Series, float
              Containing time as floating point number of days
          period : float
              Number of days of the period
          series_order : int
              Number of fourier components
      Returns
      -------
          np.array
              Matrix with seasonality features
      """
      features = np.column_stack(
          [
              fun((2.0 * (i + 1) * np.pi * t / period))
              for i in range(series_order)
              for fun in (np.sin, np.cos)
          ]
      )
      return features
  
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 3 for calling a global function with example usage: fourier_series_t(t, period, series_order) and returns: features