code: def metric_threshold_class_test_spec(request):
      """
      Test specification for MetricThreshold class:
      :return: (
                  class_params: A dictonary mapping MetricThreshold class parameter names to values,
                  expected_failure_message: expected failure message
               )
      """
      class_params = {
          "threshold": 1,
          "min_absolute_change": 1,
          "min_relative_change": 0.1,
          "greater_is_better": True,
      }
  
      if request.param == "threshold_is_not_number":
          class_params["threshold"] = "string"
          expected_failure_message = (
              "`threshold` parameter must be a number."
          )
      if request.param == "min_absolute_change_is_not_number":
          class_params["min_absolute_change"] = "string"
          expected_failure_message = "`min_absolute_change` parameter must be a positive number."
      elif request.param == "min_absolute_change_is_not_positive":
          class_params["min_absolute_change"] = -1
          expected_failure_message = "`min_absolute_change` parameter must be a positive number."
      elif request.param == "min_relative_change_is_not_float":
          class_params["min_relative_change"] = 2
          expected_failure_message = "`min_relative_change` parameter must be a floating point number."
      elif (
          request.param == "min_relative_change_is_not_between_0_and_1"
      ):
          class_params["min_relative_change"] = -0.1
          expected_failure_message = (
              "`min_relative_change` parameter must be between 0 and 1."
          )
      elif request.param == "greater_is_better_is_not_defined":
          class_params["greater_is_better"] = None
          expected_failure_message = (
              "`greater_is_better` parameter must be defined."
          )
      elif request.param == "greater_is_better_is_not_bool":
          class_params["greater_is_better"] = 1
          expected_failure_message = (
              "`greater_is_better` parameter must be a boolean."
          )
      elif request.param == "no_threshold":
          class_params["threshold"] = None
          class_params["min_absolute_change"] = None
          class_params["min_relative_change"] = None
          expected_failure_message = "no threshold was specified."
  
      return (class_params, expected_failure_message)
  
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 1 for calling a global function with example usage: metric_threshold_class_test_spec(request) and returns: class_params,  expected_failure_message