code: def test_plot_lag_reg(plotting_backend):
      log.info(
          f"testing: Plotting with lagged regressors with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=2,
          n_lags=3,
          weekly_seasonality=False,
          daily_seasonality=False,
      )
      df["A"] = df["y"].rolling(7, min_periods=1).mean()
      df["B"] = df["y"].rolling(30, min_periods=1).mean()
      m = m.add_lagged_regressor(names="A")
      m = m.add_lagged_regressor(names="B")
      m.fit(df, freq="D")
      future = m.make_future_dataframe(df, n_historic_predictions=10)
      forecast = m.predict(future)
  
      fig1 = m.plot_components(
          forecast, plotting_backend=plotting_backend
      )
      fig2 = m.plot(forecast, plotting_backend=plotting_backend)
      fig3 = m.plot_parameters(plotting_backend=plotting_backend)
  
      m.highlight_nth_step_ahead_of_each_forecast(None)
      future = m.make_future_dataframe(df, n_historic_predictions=10)
      forecast = m.predict(future)
      fig4 = m.plot_components(
          forecast,
          forecast_in_focus=2,
          plotting_backend=plotting_backend,
      )
  
      if PLOT:
          fig1.show()
          fig2.show()
          fig3.show()
          fig4.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 11 for calling a global function with example usage: test_plot_lag_reg(plotting_backend) without return types