code: def bias_config():
      return BiasConfig(
          label_values_or_threshold=[1],
          facet_name="F1",
          facet_values_or_threshold=[0.5],
          group_name="F2",
      )
  
imports: from __future__ import absolute_import
  import json
  import os
  import time
  import pytest
  from sagemaker import KMeans, s3, get_execution_role
  from sagemaker.mxnet import MXNet
  from sagemaker.pytorch import PyTorchModel
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.transformer import Transformer
  from sagemaker.estimator import Estimator
  from sagemaker.inputs import BatchDataCaptureConfig
  from sagemaker.xgboost import XGBoostModel
  from sagemaker.utils import unique_name_from_base
  from tests.integ import datasets, DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, TRANSFORM_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.kms_utils import bucket_with_encryption, get_or_create_kms_key
  from tests.integ.timeout import timeout, timeout_and_delete_model_with_transformer
  from tests.integ.vpc_test_utils import get_or_create_vpc_resources
  from sagemaker.model_monitor import DatasetFormat, Statistics, Constraints
  from sagemaker.workflow.check_job_config import CheckJobConfig
  from sagemaker.workflow.quality_check_step import DataQualityCheckConfig, ModelQualityCheckConfig
  from sagemaker.workflow.parameters import ParameterString
  from sagemaker.s3 import S3Uploader
  from sagemaker.clarify import BiasConfig, DataConfig
  from sagemaker.workflow.clarify_check_step import DataBiasCheckConfig
label_desc: Use image node_id 10 for calling a global function with example usage: bias_config() and returns: BiasConfig