code: def test_predict_raw():
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
  
      # no quantiles
      m = NeuralProphet(
          n_forecasts=12,
          n_lags=24,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      log.info("Testing raw prediction without any quantiles")
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, periods=30, n_historic_predictions=100
      )
      m.predict(df=future, raw=True)
  
      # with quantiles
      m = NeuralProphet(
          n_forecasts=12,
          n_lags=24,
          quantiles=[0.9, 0.1],
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      log.info("Testing raw prediction with some quantiles")
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, periods=30, n_historic_predictions=100
      )
      m.predict(df=future, raw=True)
  
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 40 for calling a global function with example usage: test_predict_raw() without return types