code: def test_global_modeling_global_normalization():
      # GLOBAL MODELLING - NO EXOGENOUS VARIABLES - GLOBAL NORMALIZATION
      log.info("Global Modeling + Global Normalization")
      df = pd.read_csv(PEYTON_FILE, nrows=512)
      df1_0 = df.copy(deep=True)
      df1_0["ID"] = "df1"
      df2_0 = df.copy(deep=True)
      df2_0["ID"] = "df2"
      df3_0 = df.copy(deep=True)
      df3_0["ID"] = "df3"
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=2,
          n_lags=10,
          global_normalization=True,
          trend_global_local="global",
          season_global_local="global",
          daily_seasonality=True,
          weekly_seasonality=True,
          yearly_seasonality=True,
      )
      train_df = pd.concat((df1_0, df2_0))
      test_df = df3_0
      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)
      m.plot_parameters()
      m.plot_parameters(df_name="df1")
  
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 28 for calling a global function with example usage: test_global_modeling_global_normalization() without return types