code: def test_auto_prophet_predict_evaluate(self):
      data, expect_horizon = get_data()
      from torchmetrics.functional import mean_squared_error
      import torch
  
      def customized_metric(y_true, y_pred):
          return mean_squared_error(
              torch.from_numpy(y_pred), torch.from_numpy(y_true)
          ).numpy()
  
      auto_prophet = AutoProphet(
          metric=customized_metric,
          metric_mode="min",
          changepoint_prior_scale=hp.loguniform(0.001, 0.5),
          seasonality_prior_scale=hp.loguniform(0.01, 10),
          holidays_prior_scale=hp.loguniform(0.01, 10),
          seasonality_mode=hp.choice(["additive", "multiplicative"]),
          changepoint_range=hp.uniform(0.8, 0.95),
      )
  
      auto_prophet.fit(
          data=data,
          cross_validation=False,
          expect_horizon=expect_horizon,
          n_sampling=1,
      )
  
      auto_prophet.predict(horizon=1, freq="D")
      test_data = pd.DataFrame(
          pd.date_range("20150101", periods=10), columns=["ds"]
      )
      test_data.insert(1, "y", np.random.rand(10))
      auto_prophet.evaluate(test_data)
  
file_path: BigDL/python/chronos/test/bigdl/chronos/autots/model/test_auto_prophet.py
imports: from bigdl.chronos.utils import LazyImport
  import os
  import numpy
  import pandas
  import tempfile
  from unittest import TestCase
  from ...None import op_distributed, op_diff_set_all
label_desc: Use image node_id 4 for calling the TestAutoProphet obj's underlying member method code with example usage: obj.test_auto_prophet_predict_evaluate() and returns: mean_squared_error