code: def test_post_training_bias(
      clarify_processor,
      data_config,
      data_bias_config,
      model_config,
      model_predicted_label_config,
      sagemaker_session,
  ):
      with timeout.timeout(minutes=CLARIFY_DEFAULT_TIMEOUT_MINUTES):
          clarify_processor.run_post_training_bias(
              data_config,
              data_bias_config,
              model_config,
              model_predicted_label_config,
              job_name=utils.unique_name_from_base(
                  "clarify-posttraining-bias"
              ),
              wait=True,
          )
          analysis_result_json = s3.S3Downloader.read_file(
              data_config.s3_output_path + "/analysis.json",
              sagemaker_session,
          )
          analysis_result = json.loads(analysis_result_json)
          assert (
              math.fabs(
                  analysis_result["post_training_bias_metrics"][
                      "facets"
                  ]["F1"][0]["metrics"][0]["value"]
              )
              <= 1.0
          )
          check_analysis_config(
              data_config, sagemaker_session, "post_training_bias"
          )
  
imports: from __future__ import print_function, absolute_import
  import json
  import math
  import numpy
  import os
  import pandas
  import pytest
  import statistics
  import tempfile
  from sagemaker import s3
  from sagemaker.clarify import SageMakerClarifyProcessor, BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig
  from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerPredictor
  from sagemaker import utils
  from tests import integ
  from tests.integ import timeout
label_desc: Use image node_id 41 for calling a global function with example usage: test_post_training_bias(clarify_processor, data_config, data_bias_config, model_config, model_predicted_label_config, sagemaker_session) without return types