code: def test_global_modeling_with_events_and_future_regressors():
      # GLOBAL MODELLING + REGRESSORS + EVENTS
      log.info("Global Modeling + Events + Regressors")
      df = pd.read_csv(PEYTON_FILE, nrows=512)
      df1 = df.iloc[:128, :].copy(deep=True)
      df2 = df.iloc[128:256, :].copy(deep=True)
      df3 = df.iloc[256:384, :].copy(deep=True)
      df4 = df.iloc[384:, :].copy(deep=True)
      df1["A"] = df1["y"].rolling(30, min_periods=1).mean()
      df2["A"] = df2["y"].rolling(10, min_periods=1).mean()
      df3["A"] = df3["y"].rolling(40, min_periods=1).mean()
      df4["A"] = df4["y"].rolling(20, min_periods=1).mean()
      df1["ID"] = "df1"
      df2["ID"] = "df2"
      df3["ID"] = "df1"
      df4["ID"] = "df2"
      future_regressors_df3 = pd.DataFrame(
          data={"A": df3["A"].iloc[:30]}
      )
      future_regressors_df4 = pd.DataFrame(
          data={"A": df4["A"].iloc[:40]}
      )
      future_regressors_df3["ID"] = "df1"
      future_regressors_df4["ID"] = "df2"
      playoffs_history = pd.DataFrame(
          {
              "event": "playoff",
              "ds": pd.to_datetime(
                  [
                      "2007-12-13",
                      "2008-05-31",
                      "2008-06-04",
                      "2008-06-06",
                      "2008-06-09",
                      "2008-12-13",
                      "2008-12-25",
                      "2009-01-01",
                      "2009-01-15",
                      "2009-03-20",
                      "2009-04-20",
                      "2009-05-20",
                  ]
              ),
          }
      )
      history_events_df1 = playoffs_history.iloc[:3, :].copy(deep=True)
      history_events_df2 = playoffs_history.iloc[3:6, :].copy(deep=True)
      history_events_df3 = playoffs_history.iloc[6:9, :].copy(deep=True)
      history_events_df4 = playoffs_history.iloc[9:, :].copy(deep=True)
      playoffs_future = pd.DataFrame(
          {
              "event": "playoff",
              "ds": pd.to_datetime(
                  [
                      "2008-06-10",
                      "2008-06-11",
                      "2008-12-15",
                      "2008-12-16",
                      "2009-01-26",
                      "2009-01-27",
                      "2009-06-05",
                      "2009-06-06",
                  ]
              ),
          }
      )
      future_events_df3 = playoffs_future.iloc[4:6, :].copy(deep=True)
      future_events_df4 = playoffs_future.iloc[6:8, :].copy(deep=True)
      future_events_df3["ID"] = "df1"
      future_events_df4["ID"] = "df2"
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          trend_global_local="global",
          season_global_local="global",
      )
      m = m.add_events(["playoff"])
      m = m.add_future_regressor(name="A")
      history_df1 = m.create_df_with_events(df1, history_events_df1)
      history_df2 = m.create_df_with_events(df2, history_events_df2)
      history_df3 = m.create_df_with_events(df3, history_events_df3)
      history_df4 = m.create_df_with_events(df4, history_events_df4)
      m.fit(pd.concat((history_df1, history_df2)), freq="D")
      future = m.make_future_dataframe(
          pd.concat((history_df3, history_df4)),
          n_historic_predictions=True,
          events_df=pd.concat((future_events_df3, future_events_df4)),
          regressors_df=pd.concat(
              (future_regressors_df3, future_regressors_df4)
          ),
      )
      forecast = m.predict(future)
      if PLOT:
          for key, df in forecast.groupby("ID"):
              m.plot(df)
              m.plot_parameters(df_name=key)
              m.plot_parameters()
  
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 32 for calling a global function with example usage: test_global_modeling_with_events_and_future_regressors() without return types