code: def test_lag_reg_deep():
      log.info("testing: List of Lagged Regressors (deep)")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          n_forecasts=10,
          n_lags=14,
          lagged_reg_layers=[32, 32],
          ar_layers=[32, 32],
          weekly_seasonality=False,
          daily_seasonality=False,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          quantiles=[0.1, 0.9],
      )
      df["A"] = df["y"].rolling(7, min_periods=1).mean()
      df["B"] = df["y"].rolling(15, min_periods=1).mean()
      df["C"] = df["y"].rolling(30, min_periods=1).mean()
      cols = [col for col in df.columns if col not in ["ds", "y"]]
      m = m.add_lagged_regressor(names=cols)
      m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
      m.fit(df, freq="D")
      m.predict(df)
      if PLOT:
          # print(forecast.to_string())
          # m.plot_latest_forecast(forecast, include_previous_forecasts=10)
          # 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 14 for calling a global function with example usage: test_lag_reg_deep() without return types