code: def test_forecast_tcmf_distributed(self):
      input = dict({"id": self.id, "y": self.data})
  
      from bigdl.orca import init_orca_context, stop_orca_context
  
      init_orca_context(
          cores=4,
          spark_log_level="INFO",
          init_ray_on_spark=True,
          object_store_memory="1g",
      )
      self.model.fit(input, num_workers=4, **self.fit_params)
  
      with tempfile.TemporaryDirectory() as tempdirname:
          self.model.save(tempdirname)
          loaded_model = TCMFForecaster.load(
              tempdirname, is_xshards_distributed=False
          )
      yhat = self.model.predict(horizon=self.horizon, num_workers=4)
      yhat_loaded = loaded_model.predict(
          horizon=self.horizon, num_workers=4
      )
      yhat_id = yhat_loaded["id"]
      np.testing.assert_equal(yhat_id, self.id)
      yhat = yhat["prediction"]
      yhat_loaded = yhat_loaded["prediction"]
      assert yhat.shape == (self.num_samples, self.horizon)
      np.testing.assert_equal(yhat, yhat_loaded)
  
      self.model.fit_incremental({"y": self.data_new})
      yhat_incr = self.model.predict(horizon=self.horizon)
      yhat_incr = yhat_incr["prediction"]
      assert yhat_incr.shape == (self.num_samples, self.horizon)
      np.testing.assert_raises(
          AssertionError, np.testing.assert_array_equal, yhat, yhat_incr
      )
  
      target_value = dict({"y": self.data_new})
      assert self.model.evaluate(
          target_value=target_value, metric=["mse"]
      )
      stop_orca_context()
  
file_path: BigDL/python/chronos/test/bigdl/chronos/forecaster/test_tcmf_forecaster.py
imports: import pytest
  import numpy
  from bigdl.chronos.utils import LazyImport
  from unittest import TestCase
  import tempfile
  import pandas
  from ..None import op_distributed, op_torch, op_diff_set_all
label_desc: Use image node_id 9 for calling the TestChronosModelTCMFForecaster obj's underlying member method code with example usage: obj.test_forecast_tcmf_distributed() without return types