code: def test_YosemiteTemps():
      df = pd.read_csv(YOS_FILE)
      m = NeuralProphet(
          n_lags=36,
          n_forecasts=12,
          changepoints_range=0.9,
          n_changepoints=30,
          weekly_seasonality=False,
      )
      df_train, df_test = m.split_df(df=df, freq="5min", valid_p=0.1)
  
      system_speed, std = get_system_speed()
      start = time.time()
      metrics = m.fit(
          df_train, validation_df=df_test, freq="5min"
      )  # , early_stopping=True)
      end = time.time()
  
      accuracy_metrics = metrics.to_dict("records")[-1]
      accuracy_metrics["time"] = round(end - start, 2)
      accuracy_metrics["system_performance"] = round(system_speed, 5)
      accuracy_metrics["system_std"] = round(std, 5)
      with open(
          os.path.join(DIR, "tests", "metrics", "YosemiteTemps.json"),
          "w",
      ) as outfile:
          json.dump(accuracy_metrics, outfile)
  
      create_metrics_plot(metrics).write_image(
          os.path.join(DIR, "tests", "metrics", "YosemiteTemps.svg")
      )
  
file_path: neural_prophet/tests/test_model_performance.py
imports: import json
  import logging
  import os
  import pathlib
  import time
  import numpy
  import pandas
  import plotly.graph_objects
  from plotly.subplots import make_subplots
  from plotly_resampler import unregister_plotly_resampler
  from neuralprophet import NeuralProphet, set_random_seed
label_desc: Use image node_id 4 for calling a global function with example usage: test_YosemiteTemps() without return types