code: def test_yosemite():
      log.info("TEST Yosemite Temps")
      df = pd.read_csv(YOS_FILE, nrows=NROWS)
      m = NeuralProphet(
          changepoints_range=0.95,
          n_changepoints=15,
          weekly_seasonality=False,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df, freq="5min")
      future = m.make_future_dataframe(
          df, periods=12 * 24, n_historic_predictions=12 * 24
      )
      forecast = m.predict(future)
      if PLOT:
          m.plot(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 19 for calling a global function with example usage: test_yosemite() without return types