code: def test_plot_latest_forecast(plotting_backend):
      log.info(
          f"testing: Plotting of latest forecast with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          n_lags=12,
          n_forecasts=6,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          quantiles=[0.05, 0.95],
      )
      m.fit(df, freq="D")
  
      future = m.make_future_dataframe(
          df, periods=30, n_historic_predictions=100
      )
      forecast = m.predict(future)
      fig1 = m.plot_latest_forecast(
          forecast,
          include_previous_forecasts=10,
          plotting_backend=plotting_backend,
      )
      fig2 = m.plot_latest_forecast(
          forecast, plotting_backend=plotting_backend
      )
      m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
      fig3 = m.plot_latest_forecast(
          forecast,
          include_previous_forecasts=10,
          plotting_backend=plotting_backend,
      )
      fig2 = m.plot_latest_forecast(
          forecast,
          include_previous_forecasts=10,
          plot_history_data=True,
          plotting_backend=plotting_backend,
      )
      fig3 = m.plot_latest_forecast(
          forecast,
          include_previous_forecasts=10,
          plot_history_data=False,
          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 17 for calling a global function with example usage: test_plot_latest_forecast(plotting_backend) without return types