code: def test_accelerator():
      log.info("testing: accelerator in Lightning (if available)")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          n_forecasts=2,
          n_lags=14,
          lagged_reg_layers=[32, 32],
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          trend_reg=0.1,
          quantiles=[0.1, 0.9],
          accelerator="auto",
      )
      df["A"] = df["y"].rolling(7, 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)
  
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 41 for calling a global function with example usage: test_accelerator() without return types