code: def test_make_future():
      df = pd.read_csv(PEYTON_FILE, nrows=100)
      df["A"] = df["y"].rolling(7, min_periods=1).mean()
      df_future_regressor = pd.DataFrame({"A": np.arange(10)})
  
      # without lags
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=10,
      )
      m = m.add_future_regressor(name="A")
      future = m.make_future_dataframe(
          df,
          periods=10,
          regressors_df=df_future_regressor,
      )
      assert len(future) == 10
  
      df = pd.read_csv(PEYTON_FILE, nrows=100)
      df["A"] = df["y"].rolling(7, min_periods=1).mean()
      df["B"] = df["y"].rolling(30, min_periods=1).min()
      df_future_regressor = pd.DataFrame({"A": np.arange(10)})
      # with lags
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=5,
          n_forecasts=3,
      )
      m = m.add_future_regressor(name="A")
      m = m.add_lagged_regressor(names="B")
      future = m.make_future_dataframe(
          df,
          n_historic_predictions=10,
          regressors_df=df_future_regressor,
      )
      assert len(future) == 10 + 5 + 3
  
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 16 for calling a global function with example usage: test_make_future() without return types