code: def test_wrapper_plots():
      log.info("testing: Wrapper plots")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = Prophet(
          epochs=EPOCHS, batch_size=BATCH_SIZE, learning_rate=LR
      )
      m.fit(df)
  
      future = m.make_future_dataframe(periods=50)
      forecast = m.predict(future)
  
      fig1 = m.plot(forecast)
      fig2 = m.plot(forecast, plotting_backend="plotly")
      fig3 = m.plot_components(forecast)
      fig4 = m.plot_components(forecast, plotting_backend="plotly")
  
      if PLOT:
          fig1.show()
          fig2.show()
          fig3.show()
          fig4.show()
  
file_path: neural_prophet/tests/test_wrapper.py
imports: import logging
  import os
  import pathlib
  import pandas
  from neuralprophet import TorchProphet
label_desc: Use image node_id 4 for calling a global function with example usage: test_wrapper_plots() without return types