code: def test_add_evaluation_metrics_from_json():
      evaluation_job = EvaluationJob(name=EVALUATION_JOB_NAME)
      json_path = os.path.join(
          DATA_DIR, "evaluation_metrics/clarify_bias.json"
      )
      evaluation_job.add_metric_group_from_json(
          json_path, EvaluationMetricTypeEnum.CLARIFY_BIAS
      )
  
      assert len(evaluation_job.metric_groups) == 3
      assert (
          len(
              evaluation_job.get_metric_group(
                  "post_training_bias_metrics - label Target = 1 and facet Gender=0"
              ).metric_data
          )
          == 1
      )
      assert (
          len(
              evaluation_job.get_metric_group(
                  "pre_training_bias_metrics - label Target = 1 and facet Gender=0"
              ).metric_data
          )
          == 1
      )
  
imports: from __future__ import absolute_import
  import io
  import os
  import json
  import datetime
  import re
  import logging
  import pytest
  from mock import patch, Mock
  from botocore.exceptions import ClientError
  import botocore.response
  from sagemaker.model_card import schema_constraints
  from sagemaker.model_card import Environment, ModelOverview, IntendedUses, BusinessDetails, ObjectiveFunction, TrainingMetric, HyperParameter, Metric, TrainingDetails, MetricGroup, EvaluationJob, AdditionalInformation, ModelCard, Function, TrainingJobDetails, ModelPackage
  from sagemaker.model_card.model_card import ModelCardExportJob, ModelPackageCreator, SourceAlgorithm, Container, InferenceSpecification
  from sagemaker.model_card.helpers import _MaxSizeArray, _IsList, _OneOf, _IsModelCardObject, _JSONEncoder, _hash_content_str, _DefaultToRequestDict, _SkipEncodingDecoding
  from sagemaker.model_card.evaluation_metric_parsers import EvaluationMetricTypeEnum, EVALUATION_METRIC_PARSERS
label_desc: Use image node_id 38 for calling a global function with example usage: test_add_evaluation_metrics_from_json() without return types