code: def test_forecast_ndarray_error(self):
      # is_xshards_distributed
      with self.assertRaises(Exception) as context:
          self.model.is_xshards_distributed()
      print(str(context.exception))
      self.assertTrue(
          "You should run fit before calling is_xshards_distributed()"
          in str(context.exception)
      )
  
      # fit
      input = dict({"data": self.data})
      with self.assertRaises(Exception) as context:
          self.model.fit(input)
      self.assertTrue(
          "key `y` doesn't exist in x" in str(context.exception)
      )
  
      input = dict({"y": "abc"})
      with self.assertRaises(Exception) as context:
          self.model.fit(input)
      self.assertTrue(
          "the value of y should be an ndarray"
          in str(context.exception)
      )
  
      id_diff = np.arange(200)
      input = dict({"id": id_diff, "y": self.data})
      with self.assertRaises(Exception) as context:
          self.model.fit(input)
      self.assertTrue(
          "the length of the id array should be equal to the number of"
          in str(context.exception)
      )
  
      input_right = dict({"id": self.id, "y": self.data})
      self.model.fit(input_right, **self.fit_params)
      with self.assertRaises(Exception) as context:
          self.model.fit(input_right)
      self.assertTrue(
          "This model has already been fully trained"
          in str(context.exception)
      )
  
      # fit_incremental
      data_id_diff = {"id": self.id - 1, "y": self.data_new}
      with self.assertRaises(RuntimeError) as context:
          self.model.fit_incremental(data_id_diff)
      self.assertTrue(
          "The input ids in fit_incremental differs from input ids in fit"
          in str(context.exception)
      )
  
      # evaluate
      target_value_fake = dict({"data": self.data_new})
      with self.assertRaises(Exception) as context:
          self.model.evaluate(
              target_value=target_value_fake, metric=["mse"]
          )
      self.assertTrue(
          "key `y` doesn't exist in x" in str(context.exception)
      )
  
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 6 for calling the TestChronosModelTCMFForecaster obj's underlying member method code with example usage: obj.test_forecast_ndarray_error() without return types