code: def test_validation_faulty_validation_thresholds(
      multiclass_logistic_regressor_model_uri,
      iris_dataset,
      validation_thresholds,
  ):
      with mock.patch.object(
          _model_evaluation_registry,
          "_registry",
          {"test_evaluator1": MockEvaluator},
      ):
          with pytest.raises(
              MlflowException,
              match="The validation thresholds argument",
          ):
              evaluate(
                  multiclass_logistic_regressor_model_uri,
                  data=iris_dataset._constructor_args["data"],
                  model_type="classifier",
                  targets=iris_dataset._constructor_args["targets"],
                  validation_thresholds=validation_thresholds,
              )
  
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 5 for calling a global function with example usage: test_validation_faulty_validation_thresholds(multiclass_logistic_regressor_model_uri, iris_dataset, validation_thresholds) without return types