code: def test_add_lagged_regressors():
      NROWS = 512
      EPOCHS = 3
      BATCH_SIZE = 32
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      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()
      col_dict = {
          "1": "A",
          "2": ["B"],
          "3": ["A", "B", "C"],
      }
      for key, value in col_dict.items():
          log.debug(value)
          if isinstance(value, list):
              feats = np.array(["ds", "y"] + value)
          else:
              feats = np.array(["ds", "y", value])
          df1 = pd.DataFrame(df, columns=feats)
          cols = [col for col in df1.columns if col not in ["ds", "y"]]
          m = NeuralProphet(
              n_forecasts=1,
              n_lags=3,
              weekly_seasonality=False,
              daily_seasonality=False,
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
          )
          m = m.add_lagged_regressor(names=cols)
          m.fit(df1, freq="D", validation_df=df1[-100:])
          future = m.make_future_dataframe(
              df1, n_historic_predictions=365
          )
          # Check if the future dataframe contains all the lagged regressors
          check = any(item in future.columns for item in cols)
          m.predict(future)
          log.debug(check)
  
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 4 for calling a global function with example usage: test_add_lagged_regressors() without return types