code: def test_wrong_option_global_local_modeling():
      # SEASONALITY GLOBAL LOCAL MODELLING - NO EXOGENOUS VARIABLES
      log.info("Global Modeling + Global Normalization")
      df = pd.read_csv(PEYTON_FILE, nrows=512)
      df1_0 = df.iloc[:128, :].copy(deep=True)
      df1_0["ID"] = "df1"
      df2_0 = df.iloc[128:256, :].copy(deep=True)
      df2_0["ID"] = "df2"
      df3_0 = df.iloc[256:384, :].copy(deep=True)
      df3_0["ID"] = "df3"
      m = NeuralProphet(
          n_forecasts=2,
          n_lags=10,
          growth="discontinuous",
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          season_global_local="glocsl",
          trend_global_local="glocsl",
      )
      train_df, test_df = m.split_df(
          pd.concat((df1_0, df2_0, df3_0)),
          valid_p=0.33,
          local_split=True,
      )
      m.fit(train_df)
      future = m.make_future_dataframe(test_df)
      m.predict(future)
      m.test(test_df)
      m.predict_trend(test_df)
      m.predict_seasonal_components(test_df)
  
file_path: neural_prophet/tests/test_glocal.py
imports: import logging
  import os
  import pathlib
  import pandas
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 7 for calling a global function with example usage: test_wrong_option_global_local_modeling() without return types