code: def test_global_modeling_with_lagged_regressors():
      # GLOBAL MODELLING + REGRESSORS
      log.info("Global Modeling + 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()
      df5 = df2.copy(deep=True)
      df5["A"] = 1
      df6 = df4.copy(deep=True)
      df6["A"] = 1
      df1["ID"] = "df1"
      df2["ID"] = "df2"
      df3["ID"] = "df1"
      df4["ID"] = "df2"
      df5["ID"] = "df2"
      df6["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"
      train_input = {
          0: df1,
          1: pd.concat((df1, df2)),
          2: pd.concat((df1, df2)),
          3: pd.concat((df1, df5)),
      }
      test_input = {
          0: df3,
          1: df3,
          2: pd.concat((df3, df4)),
          3: pd.concat((df3, df6)),
      }
      regressors_input = {
          0: future_regressors_df3,
          1: future_regressors_df3,
          2: pd.concat((future_regressors_df3, future_regressors_df4)),
          3: pd.concat((future_regressors_df3, future_regressors_df4)),
      }
      info_input = {
          0: "Testing single ts df train / single ts df test - single df regressors, no events",
          1: "Testing many ts df train / many ts df test - single df regressors, no events",
          2: "Testing many ts df train / many ts df test - many df regressors, no events",
          3: "Testing lagged regressor with only unique values",
      }
      for i in range(0, 4):
          log.info(info_input[i])
          m = NeuralProphet(
              n_lags=5,
              n_forecasts=3,
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
              trend_global_local="global",
              season_global_local="global",
              global_normalization=True if i == 3 else False,
          )
          m = m.add_lagged_regressor(names="A")
          m.fit(train_input[i], freq="D")
          future = m.make_future_dataframe(
              test_input[i],
              n_historic_predictions=True,
              regressors_df=regressors_input[i],
          )
          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()
      # Possible errors with regressors
      m = NeuralProphet(
          n_lags=5,
          n_forecasts=3,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m = m.add_lagged_regressor(names="A")
      m.fit(pd.concat((df1, df2)), freq="D")
      future = m.make_future_dataframe(
          pd.concat((df3, df4)),
          n_historic_predictions=True,
          regressors_df=future_regressors_df3,
      )
      log.info(
          "global model regressors with regressors df with not all IDs from original df"
      )
      future_regressors_df3["ID"] = "dfn"
      with pytest.raises(ValueError):
          future = m.make_future_dataframe(
              df3,
              n_historic_predictions=True,
              regressors_df=future_regressors_df3,
          )
      log.info("Error - key for regressors not valid")
  
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 30 for calling a global function with example usage: test_global_modeling_with_lagged_regressors() without return types