code: def test_events():
      log.info("testing: Events")
      df = pd.read_csv(PEYTON_FILE)[-NROWS:]
      playoffs = pd.DataFrame(
          {
              "event": "playoff",
              "ds": pd.to_datetime(
                  [
                      "2008-01-13",
                      "2009-01-03",
                      "2010-01-16",
                      "2010-01-24",
                      "2010-02-07",
                      "2011-01-08",
                      "2013-01-12",
                      "2014-01-12",
                      "2014-01-19",
                      "2014-02-02",
                      "2015-01-11",
                      "2016-01-17",
                      "2016-01-24",
                      "2016-02-07",
                  ]
              ),
          }
      )
      superbowls = pd.DataFrame(
          {
              "event": "superbowl",
              "ds": pd.to_datetime(
                  ["2010-02-07", "2014-02-02", "2016-02-07"]
              ),
          }
      )
      events_df = pd.concat((playoffs, superbowls))
      m = NeuralProphet(
          n_lags=2,
          n_forecasts=30,
          daily_seasonality=False,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      # set event windows
      m = m.add_events(
          ["superbowl", "playoff"],
          lower_window=-1,
          upper_window=1,
          mode="multiplicative",
          regularization=0.5,
      )
      # add the country specific holidays
      m = m.add_country_holidays(
          "US", mode="additive", regularization=0.5
      )
      m.add_country_holidays("Indonesia")
      # m.add_country_holidays("Thailand") # holidays package has issue with int input for timedelta. accepts np.float64()
      m.add_country_holidays("Philippines")
      m.add_country_holidays("Pakistan")
      m.add_country_holidays("Belarus")
      history_df = m.create_df_with_events(df, events_df)
      m.fit(history_df, freq="D")
      future = m.make_future_dataframe(
          df=history_df,
          events_df=events_df,
          periods=30,
          n_historic_predictions=90,
      )
      forecast = m.predict(df=future)
      log.debug(f"Event Parameters:: {m.model.event_params}")
      if PLOT:
          m.plot_components(forecast)
          m.plot(forecast)
          m.plot_parameters()
          plt.show()
  
imports: import logging
  import math
  import os
  import pathlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  import torch
  from neuralprophet import NeuralProphet, df_utils, set_random_seed
  from neuralprophet.data.process import _handle_missing_data, _validate_column_name
label_desc: Use image node_id 15 for calling a global function with example usage: test_events() without return types