code: def test_ar():
      log.info("testing: AR")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          n_forecasts=7,
          n_lags=7,
          yearly_seasonality=False,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
      m.fit(df, freq="D")
      future = m.make_future_dataframe(df, n_historic_predictions=90)
      forecast = m.predict(df=future)
      if PLOT:
          m.plot_latest_forecast(forecast, include_previous_forecasts=3)
          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 10 for calling a global function with example usage: test_ar() without return types