class_docstr: Module level tests for all sktime classifiers.
docstr: Test univariate classifiers raise exception on multivariate X.
docstr_tok: ["Test","univariate","classifiers","raise","exception","on","multivariate","X","."]
code: def test_multivariate_input_exception(self, estimator_instance):
      """Test univariate classifiers raise exception on multivariate X."""
      # check if multivariate input raises error for univariate classifiers
  
      # if handles multivariate, no error is to be raised
      #   that classifier works on multivariate data is tested in test_all_estimators
      if estimator_instance.get_tag("capability:multivariate"):
          return None
  
      error_msg = "multivariate series"
  
      scenario = ClassifierFitPredictMultivariate()
  
      # check if estimator raises appropriate error message
      #   composites will raise a warning, non-composites an exception
      if estimator_instance.is_composite():
          with pytest.warns(UserWarning, match=error_msg):
              scenario.run(estimator_instance, method_sequence=["fit"])
      else:
          with pytest.raises(ValueError, match=error_msg):
              scenario.run(estimator_instance, method_sequence=["fit"])
  
imports: import numpy
  import pandas
  import pytest
  from sktime.classification.tests._expected_outputs import basic_motions_proba, unit_test_proba
  from sktime.datasets import load_basic_motions, load_unit_test
  from sktime.datatypes import check_is_scitype
  from sktime.tests.test_all_estimators import BaseFixtureGenerator, QuickTester
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils._testing.panel import make_classification_problem
  from sktime.utils._testing.scenarios_classification import ClassifierFitPredictMultivariate
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 for calling the TestAllClassifiers obj's underlying member method code with example usage: obj.test_multivariate_input_exception(estimator_instance) and returns: None