code: def test_plot_global_local_parameters(plotting_backend):
      log.info(
          f"Plotting global modeling + global normalization with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=512)
      df1_0 = df.copy(deep=True)
      df1_0["ID"] = "df1"
      df2_0 = df.copy(deep=True)
      df2_0["ID"] = "df2"
      df3_0 = df.copy(deep=True)
      df3_0["ID"] = "df3"
      m = NeuralProphet(
          n_forecasts=2,
          n_lags=10,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          trend_global_local="local",
          season_global_local="local",
          weekly_seasonality=True,
          daily_seasonality=True,
          yearly_seasonality=True,
      )
      train_df, test_df = m.split_df(
          pd.concat((df1_0, df2_0, df3_0)),
          valid_p=0.33,
          local_split=True,
      )
      m.fit(train_df)
      future = m.make_future_dataframe(test_df)
      forecast = m.predict(future)
  
      fig1 = m.plot_parameters(
          df_name="df1", plotting_backend=plotting_backend
      )
      fig2 = m.plot_parameters(plotting_backend=plotting_backend)
      fig3 = m.plot_components(
          forecast, df_name="df1", plotting_backend=plotting_backend
      )
      log.info(f"Plotting global modeling with {plotting_backend}")
      df1 = df.copy(deep=True)
      df1["ID"] = "df1"
      df2 = df.copy(deep=True)
      df2["ID"] = "df2"
      df_global = pd.concat((df1, df2))
      m = NeuralProphet(
          n_forecasts=7,
          n_lags=14,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df_global, freq="D")
      future = m.make_future_dataframe(
          df_global, periods=m.n_forecasts, n_historic_predictions=10
      )
      forecast = m.predict(future)
  
      log.info(
          f"Plot forecast with many IDs with {plotting_backend} - Raise exceptions"
      )
      with pytest.raises(Exception):
          m.plot(forecast)
      with pytest.raises(Exception):
          m.plot_latest_forecast(
              forecast, include_previous_forecasts=10
          )
      with pytest.raises(Exception):
          m.plot_components(forecast)
      forecast = m.predict(df_global)
      with pytest.raises(Exception):
          m.plot(forecast)
      with pytest.raises(Exception):
          m.plot_latest_forecast(
              forecast, include_previous_forecasts=10
          )
      with pytest.raises(Exception):
          m.plot_components(forecast)
  
      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 4 for calling a global function with example usage: test_plot_global_local_parameters(plotting_backend) without return types