class_docstr: Data class to hold some essential analysis configuration of ClarifyBaseliningJob
class_docstr_tok: ["Data","class","to","hold","some","essential","analysis","configuration","of","ClarifyBaseliningJob"]
docstr: Initialization.
  
  Args:
      analysis_config (BiasAnalysisConfig or ExplainabilityAnalysisConfig): analysis config
          from configurations of the baselining job.
      features_attribute (str): JMESPath expression to locate features in predictor request
          payload. Only required when predictor content type is JSONlines.
      inference_attribute (str): Index, header or JMESPath expression to locate predicted
          label in predictor response payload.
      probability_attribute (str): Index or JMESPath expression to locate probabilities or
          scores in the model output for computing feature attribution.
      probability_threshold_attribute (float): Value to indicate the threshold to select
          the binary label in the case of binary classification. Default is 0.5.
code: def __init__(
      self,
      analysis_config,
      features_attribute=None,
      inference_attribute=None,
      probability_attribute=None,
      probability_threshold_attribute=None,
  ):
      """Initialization.
  
      Args:
          analysis_config (BiasAnalysisConfig or ExplainabilityAnalysisConfig): analysis config
              from configurations of the baselining job.
          features_attribute (str): JMESPath expression to locate features in predictor request
              payload. Only required when predictor content type is JSONlines.
          inference_attribute (str): Index, header or JMESPath expression to locate predicted
              label in predictor response payload.
          probability_attribute (str): Index or JMESPath expression to locate probabilities or
              scores in the model output for computing feature attribution.
          probability_threshold_attribute (float): Value to indicate the threshold to select
              the binary label in the case of binary classification. Default is 0.5.
      """
      self.analysis_config = analysis_config
      self.features_attribute = features_attribute
      self.inference_attribute = inference_attribute
      self.probability_attribute = probability_attribute
      self.probability_threshold_attribute = (
          probability_threshold_attribute
      )
  
imports: from __future__ import print_function, absolute_import
  import copy
  import json
  import logging
  import uuid
  from sagemaker.model_monitor import model_monitoring
  from sagemaker import image_uris, s3
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, format_tags
  from sagemaker.clarify import SageMakerClarifyProcessor, ModelPredictedLabelConfig
  from sagemaker.lineage._utils import get_resource_name_from_arn
label_desc: Use image node_id 1 to create a new ClarifyBaseliningConfig object  with example: obj = ClarifyBaseliningConfig(analysis_config, features_attribute, inference_attribute, probability_attribute, probability_threshold_attribute)