code: def test_validation_value_threshold_should_pass(
      multiclass_logistic_regressor_model_uri,
      iris_dataset,
      value_threshold_test_spec,
  ):
      metrics, validation_thresholds, _ = value_threshold_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=None
          )
          with mock.patch.object(
              MockEvaluator, "can_evaluate", return_value=True
          ), mock.patch.object(
              MockEvaluator,
              "evaluate",
              return_value=evaluator1_return_value,
          ):
              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=None,
              )
  
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 8 for calling a global function with example usage: test_validation_value_threshold_should_pass(multiclass_logistic_regressor_model_uri, iris_dataset, value_threshold_test_spec) without return types