code: def test_validation_multi_thresholds_should_fail(
      multiclass_logistic_regressor_model_uri,
      iris_dataset,
      multi_thresholds_test_spec,
  ):
      (
          metrics,
          baseline_model_metrics,
          validation_thresholds,
          expected_validation_results,
      ) = multi_thresholds_test_spec
  
      with mock.patch.object(
          _model_evaluation_registry,
          "_registry",
          {"test_evaluator1": MockEvaluator},
      ):
          evaluator1_config = {}
          evaluator1_return_value = EvaluationResult(
              metrics=metrics,
              artifacts={},
              baseline_model_metrics=baseline_model_metrics,
          )
          expected_failure_message = message_separator.join(
              map(str, list(expected_validation_results.values()))
          )
          with mock.patch.object(
              MockEvaluator, "can_evaluate", return_value=True
          ) as _, mock.patch.object(
              MockEvaluator,
              "evaluate",
              return_value=evaluator1_return_value,
          ) as _:
              with pytest.raises(
                  ModelValidationFailedException,
                  match=expected_failure_message,
              ):
                  evaluate(
                      multiclass_logistic_regressor_model_uri,
                      data=iris_dataset._constructor_args["data"],
                      model_type="classifier",
                      targets=iris_dataset._constructor_args["targets"],
                      evaluators="test_evaluator1",
                      evaluator_config=evaluator1_config,
                      validation_thresholds=validation_thresholds,
                      baseline_model=multiclass_logistic_regressor_model_uri,
                  )
  
imports: from unittest import mock
  import pytest
  from mlflow.exceptions import MlflowException
  from mlflow.models.evaluation import EvaluationResult, MetricThreshold, ModelEvaluator, evaluate
  from mlflow.models.evaluation.evaluator_registry import _model_evaluation_registry
  from mlflow.models.evaluation.validation import MetricThresholdClassException, ModelValidationFailedException, _MetricValidationResult
  from tests.evaluate.test_evaluation import iris_dataset, multiclass_logistic_regressor_model_uri
label_desc: Use image node_id 16 for calling a global function with example usage: test_validation_multi_thresholds_should_fail(multiclass_logistic_regressor_model_uri, iris_dataset, multi_thresholds_test_spec) without return types