code: def test_n_lags_for_regressors():
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      df1 = df.iloc[:128, :].copy(deep=True)
      df1["A"] = df1["y"].rolling(30, min_periods=1).mean()
      n_lags_input = [2, 2, 5, 2, 1, 2, 2, 0]
      n_lags_regressors_input = [2, "auto", 2, 5, 2, 1, "scalar", 5]
      info_input = [
          "n_lags == n_lags_regressors",
          "n_lags == n_lags_regressors (auto)",
          "n_lags > n_lags_regressors",
          "n_lags < n_lags_regressors",
          "n_lags (1) < n_lags_regressors",
          "n_lags > n_lags_regressors (1)",
          "n_lags > n_lags_regressors (scalar)",
          "n_lags == 0 and n_lags_regressors > 0",
      ]
      # Testing cases with 1 covariate
      for i in range(len(info_input)):
          log.debug(info_input[i])
          m = NeuralProphet(
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
              n_forecasts=2,
              n_lags=n_lags_input[i],
          )
          m = m.add_lagged_regressor(
              names="A", n_lags=n_lags_regressors_input[i]
          )
          m.fit(df1, freq="D")
          future = m.make_future_dataframe(
              df1, n_historic_predictions=True
          )
          forecast = m.predict(df=future)
          if PLOT:
              m.plot(forecast)
              m.plot_parameters()
      # Testing case with 2 covariates
      df1["B"] = df1["y"].rolling(8, min_periods=1).mean()
      n_lags_input = [0, 2, 2, 5, 1]
      n_lags_regressors_input_A = [5, 7, 3, 3, "scalar"]
      n_lags_regressors_input_B = [7, 5, None, None, None]
      info_input = [
          "n_lags == 0 and 2 regressors with different lags between them",
          "n_lags > 0 and 2 regressors with different lags between them",
          "n_lags < lags from both regressors",
          "n_lags > lags from both regressors",
          "n_lags == lags from both regressors (scalar)",
      ]
      for i in range(len(info_input)):
          log.debug(info_input[i])
          m = NeuralProphet(
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
              n_forecasts=3,
              n_lags=n_lags_input[i],
          )
          if i < 2:
              m = m.add_lagged_regressor(
                  names="A", n_lags=n_lags_regressors_input_A[i]
              )
              m = m.add_lagged_regressor(
                  names="B", n_lags=n_lags_regressors_input_B[i]
              )
          else:
              # Testing call of add_lagged_regressor with list of names
              m = m.add_lagged_regressor(
                  names=["A", "B"], n_lags=n_lags_regressors_input_A[i]
              )
          m.fit(df1, freq="D")
          future = m.make_future_dataframe(
              df1, n_historic_predictions=True
          )
          forecast = m.predict(df=future)
          if PLOT:
              m.plot(forecast)
              m.plot_parameters()
      # Testing case with assertion error in time_dataset - n_lags = 0
      log.debug("Exception regressor n_lags == 0")
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=2,
          n_lags=2,
      )
      m = m.add_lagged_regressor(names="A", n_lags=0)
      m = m.add_lagged_regressor(names="B", n_lags=0)
      with pytest.raises(AssertionError):
          m.fit(df1, freq="D")
  
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 38 for calling a global function with example usage: test_n_lags_for_regressors() without return types