code: def make_weekly_data():
      n_obs = 52 * 4
      seed = 2020
      rw = make_trend(n_obs, rw_loc=1, rw_scale=0.1, seed=seed)
      fs = make_seasonality(
          n_obs, seasonality=52, method="fourier", order=2, seed=seed
      )
      coef = [0.2, 0.1, 0.3, 0.15, -0.2, -0.1]
      x, y, coef = make_regression(n_obs, coef, scale=2.0, seed=seed)
  
      df = pd.DataFrame(
          np.concatenate([(rw + fs + y).reshape(-1, 1), x], axis=1),
          columns=["response"] + list("abcdef"),
      )
      df["week"] = pd.date_range(
          start="2016-01-04", periods=n_obs, freq="7D"
      )
      df = df[["week", "response"] + list("abcdef")]
      train_df = df[df["week"] <= "2019-01-01"].reset_index(drop=True)
      test_df = df[df["week"] > "2019-01-01"].reset_index(drop=True)
  
      return train_df, test_df, coef
  
file_path: orbit/tests/conftest.py
imports: import pytest
  import pandas
  import numpy
  import importlib_resources
  from copy import deepcopy
  from orbit.utils.simulation import make_trend, make_seasonality, make_regression
label_desc: Use image node_id 4 for calling a global function with example usage: make_weekly_data() and returns: train_df,  test_df,  coef