class_docstr: Amazon SageMaker model monitor to monitor bias metrics of an endpoint.
  
  Please see the __init__ method of its base class for how to instantiate it.
class_docstr_tok: ["Amazon","SageMaker","model","monitor","to","monitor","bias","metrics","of","an","endpoint",".","Please","see","the","__init__","method","of","its","base","class","for","how","to","instantiate","it","."]
docstr: Suggests baselines for use with Amazon SageMaker Model Monitoring Schedules.
  
  Args:
      data_config (:class:`~sagemaker.clarify.DataConfig`): Config of the input/output data.
      bias_config (:class:`~sagemaker.clarify.BiasConfig`): Config of sensitive groups.
      model_config (:class:`~sagemaker.clarify.ModelConfig`): Config of the model and its
          endpoint to be created.
      model_predicted_label_config (:class:`~sagemaker.clarify.ModelPredictedLabelConfig`):
          Config of how to extract the predicted label from the model output.
      wait (bool): Whether the call should wait until the job completes (default: False).
      logs (bool): Whether to show the logs produced by the job.
          Only meaningful when wait is True (default: False).
      job_name (str): Processing job name. If not specified, the processor generates
          a default job name, based on the image name and current timestamp.
      kms_key (str): The ARN of the KMS key that is used to encrypt the
          user code file (default: None).
  
  Returns:
      sagemaker.processing.ProcessingJob: The ProcessingJob object representing the
          baselining job.
code: def suggest_baseline(
      self,
      data_config,
      bias_config,
      model_config,
      model_predicted_label_config=None,
      wait=False,
      logs=False,
      job_name=None,
      kms_key=None,
  ):
      """Suggests baselines for use with Amazon SageMaker Model Monitoring Schedules.
  
      Args:
          data_config (:class:`~sagemaker.clarify.DataConfig`): Config of the input/output data.
          bias_config (:class:`~sagemaker.clarify.BiasConfig`): Config of sensitive groups.
          model_config (:class:`~sagemaker.clarify.ModelConfig`): Config of the model and its
              endpoint to be created.
          model_predicted_label_config (:class:`~sagemaker.clarify.ModelPredictedLabelConfig`):
              Config of how to extract the predicted label from the model output.
          wait (bool): Whether the call should wait until the job completes (default: False).
          logs (bool): Whether to show the logs produced by the job.
              Only meaningful when wait is True (default: False).
          job_name (str): Processing job name. If not specified, the processor generates
              a default job name, based on the image name and current timestamp.
          kms_key (str): The ARN of the KMS key that is used to encrypt the
              user code file (default: None).
  
      Returns:
          sagemaker.processing.ProcessingJob: The ProcessingJob object representing the
              baselining job.
      """
      baselining_processor = self._create_baselining_processor()
      baselining_job_name = self._generate_baselining_job_name(
          job_name=job_name
      )
      baselining_processor.run_bias(
          data_config=data_config,
          bias_config=bias_config,
          model_config=model_config,
          model_predicted_label_config=model_predicted_label_config,
          wait=wait,
          logs=logs,
          job_name=baselining_job_name,
          kms_key=kms_key,
      )
  
      latest_baselining_job_config = ClarifyBaseliningConfig(
          analysis_config=BiasAnalysisConfig(
              bias_config=bias_config,
              headers=data_config.headers,
              label=data_config.label,
          ),
          features_attribute=data_config.features,
      )
      if model_predicted_label_config is not None:
          latest_baselining_job_config.inference_attribute = (
              model_predicted_label_config.label
              if model_predicted_label_config.label is None
              else str(model_predicted_label_config.label)
          )
          latest_baselining_job_config.probability_attribute = (
              model_predicted_label_config.probability
              if model_predicted_label_config.probability is None
              else str(model_predicted_label_config.probability)
          )
          latest_baselining_job_config.probability_threshold_attribute = (
              model_predicted_label_config.probability_threshold
          )
      self.latest_baselining_job_config = latest_baselining_job_config
      self.latest_baselining_job_name = baselining_job_name
      self.latest_baselining_job = ClarifyBaseliningJob(
          processing_job=baselining_processor.latest_job
      )
  
      self.baselining_jobs.append(self.latest_baselining_job)
      return baselining_processor.latest_job
  
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 2 for calling the ModelBiasMonitor obj's underlying member method code with example usage: obj.suggest_baseline(data_config, bias_config, model_config, model_predicted_label_config, wait, logs, job_name, kms_key) and returns: baselining_processor