code: def min_absolute_change_threshold_test_spec(request):
      """
      Test specification for min_absolute_change threshold tests:
      :return: (
                  metrics: A dictionary mapping scalar metric names to scalar metric values,
                  baseline_model_metrics: A dictionary mapping scalar metric names
                      to scalar metric values of baseline_model,
                  validation_thresholds: A dictonary mapping scalar metric names
                      to MetricThreshold(threshold=0.2, greater_is_better=True),
                  expected_validation_results: A dictonary mapping scalar metric names
                      to _MetricValidationResult
               )
      """
      acc_threshold = MetricThreshold(
          min_absolute_change=0.1, greater_is_better=True
      )
      f1score_threshold = MetricThreshold(
          min_absolute_change=0.15, greater_is_better=True
      )
      log_loss_threshold = MetricThreshold(
          min_absolute_change=0.1, greater_is_better=False
      )
      l1_loss_threshold = MetricThreshold(
          min_absolute_change=0.15, greater_is_better=False
      )
  
      if request.param == "single_metric_not_satisfied_higher_better":
          acc_validation_result = _MetricValidationResult(
              "accuracy", 0.79, acc_threshold, 0.7
          )
          acc_validation_result.min_absolute_change_failed = True
          return (
              {"accuracy": 0.79},
              {"accuracy": 0.7},
              {"accuracy": acc_threshold},
              {"accuracy": acc_validation_result},
          )
  
      if (
          request.param
          == "multiple_metrics_not_satisfied_higher_better"
      ):
          acc_validation_result = _MetricValidationResult(
              "accuracy", 0.79, acc_threshold, 0.7
          )
          acc_validation_result.min_absolute_change_failed = True
          f1score_validation_result = _MetricValidationResult(
              "f1_score", 0.8, f1score_threshold, 0.7
          )
          f1score_validation_result.min_absolute_change_failed = True
          return (
              {"accuracy": 0.79, "f1_score": 0.8},
              {"accuracy": 0.7, "f1_score": 0.7},
              {
                  "accuracy": acc_threshold,
                  "f1_score": f1score_threshold,
              },
              {
                  "accuracy": acc_validation_result,
                  "f1_score": f1score_validation_result,
              },
          )
  
      if request.param == "single_metric_not_satisfied_lower_better":
          l1_loss_validation_result = _MetricValidationResult(
              "custom_l1_loss", 0.5, l1_loss_threshold, 0.6
          )
          l1_loss_validation_result.min_absolute_change_failed = True
          return (
              {"custom_l1_loss": 0.5},
              {"custom_l1_loss": 0.6},
              {"custom_l1_loss": l1_loss_threshold},
              {"custom_l1_loss": l1_loss_validation_result},
          )
  
      if request.param == "multiple_metrics_not_satisfied_lower_better":
          l1_loss_validation_result = _MetricValidationResult(
              "custom_l1_loss", 0.5, l1_loss_threshold, 0.6
          )
          l1_loss_validation_result.min_absolute_change_failed = True
          log_loss_validation_result = _MetricValidationResult(
              "log_loss", 0.45, log_loss_threshold, 0.3
          )
          log_loss_validation_result.min_absolute_change_failed = True
          return (
              {"custom_l1_loss": 0.5, "log_loss": 0.45},
              {"custom_l1_loss": 0.6, "log_loss": 0.3},
              {
                  "custom_l1_loss": l1_loss_threshold,
                  "log_loss": log_loss_threshold,
              },
              {
                  "custom_l1_loss": l1_loss_validation_result,
                  "log_loss": log_loss_validation_result,
              },
          )
  
      if request.param == "equality_boundary":
          acc_validation_result = _MetricValidationResult(
              "accuracy", 0.8, acc_threshold, 0.7
          )
          log_loss_validation_result = _MetricValidationResult(
              "custom_log_loss", 0.2, log_loss_threshold, 0.3
          )
          return (
              {"accuracy": 0.8 + 1e-10, "log_loss": 0.2 - 1e-10},
              {"accuracy": 0.7, "log_loss": 0.3},
              {
                  "accuracy": acc_threshold,
                  "log_loss": log_loss_threshold,
              },
              {},
          )
  
      if request.param == "single_metric_satisfied_higher_better":
          return (
              {"accuracy": 0.9 + 1e-2},
              {"accuracy": 0.8},
              {"accuracy": acc_threshold},
              {},
          )
  
      if request.param == "single_metric_satisfied_lower_better":
          return (
              {"log_loss": 0.3},
              {"log_loss": 0.4 + 1e-3},
              {"log_loss": log_loss_threshold},
              {},
          )
  
      if request.param == "multiple_metrics_all_satisfied":
          return (
              {"accuracy": 0.9, "f1_score": 0.8, "log_loss": 0.3},
              {"accuracy": 0.7, "f1_score": 0.6, "log_loss": 0.5},
              {
                  "accuracy": acc_threshold,
                  "f1_score": f1score_threshold,
                  "log_loss": log_loss_threshold,
              },
              {},
          )
  
      if request.param == "missing_baseline_metric":
          l1_loss_validation_result = _MetricValidationResult(
              "custom_l1_loss", 0.72, l1_loss_threshold, None
          )
          l1_loss_validation_result.missing_baseline = True
          return (
              {"custom_l1_loss": 0.72},
              None,
              {"custom_l1_loss": l1_loss_threshold},
              {"custom_l1_loss": l1_loss_validation_result},
          )
  
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 9 for calling a global function with example usage: min_absolute_change_threshold_test_spec(request) and returns: dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  dict,  dict,  dict, dict,  None,  dict,  dict