code: def test_plot_parameters(plotting_backend):
      log.info(
          f"testing: Plotting parameters with forecast in focus with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=7,
          n_lags=14,
      )
      m.fit(df, freq="D")
  
      m.highlight_nth_step_ahead_of_each_forecast(7)
      future = m.make_future_dataframe(df, n_historic_predictions=10)
      m.predict(future)
  
      fig1 = m.plot_parameters(plotting_backend=plotting_backend)
  
      log.info(
          f"testing: Plotting parameters without forecast in focus with {plotting_backend}"
      )
      m.highlight_nth_step_ahead_of_each_forecast(None)
      future = m.make_future_dataframe(df, n_historic_predictions=10)
      m.predict(future)
      fig2 = m.plot_parameters(plotting_backend=plotting_backend)
  
      # select components manually
      fig3 = m.plot_parameters(
          components="trend", plotting_backend=plotting_backend
      )
  
      log.info(
          "Plot parameters with wrong component selection - Raise ValueError"
      )
      with pytest.raises(ValueError):
          m.plot_parameters(
              components=["events"], plotting_backend=plotting_backend
          )
      with pytest.raises(ValueError):
          m.plot_parameters(
              components=["trend123"], plotting_backend=plotting_backend
          )
  
      if PLOT:
          fig1.show()
          fig2.show()
          fig3.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 3 for calling a global function with example usage: test_plot_parameters(plotting_backend) without return types