code: def test_split_conformal_prediction():
      log.info("testing: Naive Split Conformal Prediction Air Travel")
      df = pd.read_csv(AIR_FILE)
      m = NeuralProphet(
          seasonality_mode="multiplicative",
          loss_func="MSE",
          quantiles=[0.05, 0.95],
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
  
      train_df, test_df = m.split_df(df, freq="MS", valid_p=0.2)
      train_df, cal_df = m.split_df(train_df, freq="MS", valid_p=0.15)
      m.fit(train_df, freq="MS")
  
      alpha = 0.1
      decompose = False
      for method in ["naive", "cqr"]:  # Naive and CQR SCP methods
          for show_all_PI in [True, False]:
              future = m.make_future_dataframe(
                  test_df,
                  periods=50,
                  n_historic_predictions=len(test_df),
              )
              forecast = m.conformal_predict(
                  future,
                  calibration_df=cal_df,
                  alpha=alpha,
                  method=method,
                  decompose=decompose,
                  show_all_PI=show_all_PI,
              )
              uncertainty_evaluate(forecast)
  
file_path: neural_prophet/tests/test_uncertainty.py
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet, uncertainty_evaluate
label_desc: Use image node_id 5 for calling a global function with example usage: test_split_conformal_prediction() without return types