code: def test_auto_arima_model_pyfunc_with_params_output(
      auto_arima_model, model_path
  ):
      """Test auto arima prediction of loaded pyfunc model with parameters."""
      from sktime.utils import mlflow_sktime
  
      auto_arima_model.pyfunc_predict_conf = {
          "predict_method": {
              "predict": {},
              "predict_interval": {"coverage": [0.1, 0.9]},
              "predict_proba": {
                  "quantiles": [0.1, 0.9],
                  "marginal": True,
              },
              "predict_quantiles": {"alpha": [0.1, 0.9]},
              "predict_var": {"cov": True},
          }
      }
      mlflow_sktime.save_model(
          sktime_model=auto_arima_model,
          path=model_path,
      )
      loaded_pyfunc = mlflow_sktime.pyfunc.load_model(
          model_uri=model_path
      )
  
      model_predict = auto_arima_model.predict()
      model_predict_interval = auto_arima_model.predict_interval(
          coverage=[0.1, 0.9]
      )
      model_predict_interval.columns = flatten_multiindex(
          model_predict_interval
      )
      model_predict_proba_dist = auto_arima_model.predict_proba()
      model_predict_proba = model_predict_proba_dist.quantile(
          [0.1, 0.9]
      )
      model_predict_proba.columns = flatten_multiindex(
          model_predict_proba
      )
      model_predict_quantiles = auto_arima_model.predict_quantiles(
          alpha=[0.1, 0.9]
      )
      model_predict_quantiles.columns = flatten_multiindex(
          model_predict_quantiles
      )
      model_predict_var = auto_arima_model.predict_var(cov=True)
      model_predictions = pd.concat(
          [
              model_predict,
              model_predict_interval,
              model_predict_proba,
              model_predict_quantiles,
              model_predict_var,
          ],
          axis=1,
          keys=[
              "model_predict",
              "model_predict_interval",
              "model_predict_proba",
              "model_predict_quantiles",
              "model_predict_var",
          ],
      )
  
      pyfunc_predict = loaded_pyfunc.predict(pd.DataFrame())
      np.testing.assert_array_equal(
          model_predictions.values, pyfunc_predict.values
      )
  
imports: import os
  import sys
  from pathlib import Path
  from unittest import mock
  import numpy
  import pandas
  import pytest
  from sktime.datasets import load_airline, load_arrow_head, load_longley
  from sktime.forecasting.arima import AutoARIMA
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.split import temporal_train_test_split
  from sktime.utils.multiindex import flatten_multiindex
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 13 for calling a global function with example usage: test_auto_arima_model_pyfunc_with_params_output(auto_arima_model, model_path) without return types