code: def test_plot_trend(plotting_backend):
      log.info(
          f"testing: Plotting linear trend with {plotting_backend}"
      )
      df = pd.read_csv(AIR_FILE)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_changepoints=0,
          yearly_seasonality=2,
          seasonality_mode="multiplicative",
      )
      m.fit(df, freq="MS")
      future = m.make_future_dataframe(
          df, periods=48, n_historic_predictions=len(df) - m.n_lags
      )
      forecast = m.predict(future)
      fig1 = m.plot(forecast, plotting_backend=plotting_backend)
      fig2 = m.plot_components(
          forecast, plotting_backend=plotting_backend
      )
      fig3 = m.plot_parameters(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 8 for calling a global function with example usage: test_plot_trend(plotting_backend) without return types