code: def data_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 statistics
  import time
  import tempfile
  import pytest
  import numpy
  import pandas
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerPredictor
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, SageMakerClarifyProcessor
  from sagemaker.processing import ProcessingInput, ProcessingOutput
  from sagemaker.session import get_execution_role
  from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo
  from sagemaker.workflow.condition_step import ConditionStep, JsonGet
  from sagemaker.workflow.parameters import ParameterInteger, ParameterString
  from sagemaker.workflow.steps import ProcessingStep
  from sagemaker.workflow.properties import PropertyFile
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker import utils
  from tests import integ
  from tests.integ import timeout
label_desc: Use image node_id 7 for calling a global function with example usage: data_bias_config() and returns: BiasConfig