code: def faulty_baseline_model_param_test_spec(request):
      """
      Test specification for faulty `baseline_model` parameter tests:
      :return: (
                  validation_thresholds: A dictonary mapping scalar metric names
                      to MetricThreshold(threshold=0.2, greater_is_better=True),
                  baseline_model: value for the `baseline_model` param passed into mlflow.evaluate()
                  expected_failure_message: expected failure message
               )
      """
      if request.param == "min_relative_change_present":
          return (
              {
                  "accuracy": MetricThreshold(
                      min_absolute_change=0.1, greater_is_better=True
                  )
              },
              None,
              "The baseline model must be specified",
          )
      if request.param == "min_absolute_change_present":
          return (
              {
                  "accuracy": MetricThreshold(
                      min_relative_change=0.1, greater_is_better=True
                  )
              },
              None,
              "The baseline model must be specified",
          )
      if request.param == "both_relative_absolute_change_present":
          return (
              {
                  "accuracy": MetricThreshold(
                      min_absolute_change=0.05,
                      min_relative_change=0.1,
                      greater_is_better=True,
                  )
              },
              None,
              "The baseline model must be specified",
          )
      if request.param == "baseline_model_is_not_string":
          return (
              {
                  "accuracy": MetricThreshold(
                      min_absolute_change=0.05,
                      min_relative_change=0.1,
                      greater_is_better=True,
                  )
              },
              1.0,
              "The baseline model argument must be a string 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 3 for calling a global function with example usage: faulty_baseline_model_param_test_spec(request) and returns: dict,  None,  str, dict,  None,  str, dict,  None,  str, dict,  int,  str