code: def test_globaltimedataset():
      df = pd.read_csv(PEYTON_FILE, nrows=100)
      df1 = df[:50]
      df1 = df1.assign(ID="df1")
      df2 = df[50:]
      df2 = df2.assign(ID="df2")
      m1 = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          yearly_seasonality=True,
          weekly_seasonality=True,
          daily_seasonality=True,
      )
      m2 = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=3,
          n_forecasts=2,
      )
      m3 = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      # TODO m3.add_country_holidays("US")
      config_normalization = configure.Normalization(
          "auto", False, True, False
      )
      for m in [m1, m2, m3]:
          df_global = pd.concat((df1, df2))
          df_global["ds"] = pd.to_datetime(df_global.loc[:, "ds"])
          config_normalization.init_data_params(
              df_global,
              m.config_lagged_regressors,
              m.config_regressors,
              m.config_events,
          )
          m.config_normalization = config_normalization
          df_global = _normalize(
              df=df_global, config_normalization=m.config_normalization
          )
          _create_dataset(m, df_global, predict_mode=False)
          _create_dataset(m, df_global, predict_mode=True)
  
      # lagged_regressors, future_regressors
      df4 = df.copy()
      df4["A"] = np.arange(len(df4))
      df4["B"] = np.arange(len(df4)) * 0.1
      df4["ID"] = "df4"
      df4["ds"] = pd.to_datetime(df4.loc[:, "ds"])
      m4 = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=2,
      )
      m4.add_future_regressor("A")
      m4.add_lagged_regressor("B")
      config_normalization = configure.Normalization(
          "auto", False, True, False
      )
      for m in [m4]:
          df4
          config_normalization.init_data_params(
              df4,
              m.config_lagged_regressors,
              m.config_regressors,
              m.config_events,
          )
          m.config_normalization = config_normalization
          df4 = _normalize(
              df=df4, config_normalization=m.config_normalization
          )
          _create_dataset(m, df4, predict_mode=False)
          _create_dataset(m, df4, predict_mode=True)
  
imports: import logging
  import os
  import pathlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from torch.utils.data import DataLoader
  from neuralprophet import NeuralProphet, configure, df_utils, time_dataset
  from neuralprophet.data.process import _create_dataset, _handle_missing_data
  from neuralprophet.data.transform import _normalize
label_desc: Use image node_id 13 for calling a global function with example usage: test_globaltimedataset() without return types