code: def test_future_reg():
      log.info("testing: Future Regressors")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS + 50)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      df["A"] = df["y"].rolling(7, min_periods=1).mean()
      df["B"] = df["y"].rolling(30, min_periods=1).mean()
      regressors_df_future = pd.DataFrame(
          data={"A": df["A"][-50:], "B": df["B"][-50:]}
      )
      df = df[:-50]
      m = m.add_future_regressor(name="A")
      m = m.add_future_regressor(name="B", mode="multiplicative")
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df=df,
          regressors_df=regressors_df_future,
          n_historic_predictions=10,
          periods=50,
      )
      forecast = m.predict(df=future)
      if PLOT:
          m.plot(forecast)
          m.plot_components(forecast)
          m.plot_parameters()
          plt.show()
  
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 16 for calling a global function with example usage: test_future_reg() without return types