class_docstr: Base class of Amazon SageMaker Explainability API model monitors.
  
  This class is an ``abstract base class``, please instantiate its subclasses
  if you want to monitor bias metrics or feature attribution of an endpoint.
class_docstr_tok: ["Base","class","of","Amazon","SageMaker","Explainability","API","model","monitors",".","This","class","is","an","``","abstract","base","class","``",",","please","instantiate","its","subclasses","if","you","want","to","monitor","bias","metrics","or","feature","attribution","of","an","endpoint","."]
docstr: Not implemented.
  
  '.run_baseline()' is only allowed for ModelMonitor objects.
  Please use `suggest_baseline` instead.
  
  Raises:
      NotImplementedError
code: def run_baseline(self, **_):
      """Not implemented.
  
      '.run_baseline()' is only allowed for ModelMonitor objects.
      Please use `suggest_baseline` instead.
  
      Raises:
          NotImplementedError
      """
      raise NotImplementedError(
          "'.run_baseline()' is only allowed for ModelMonitor objects."
          "Please use suggest_baseline instead."
      )
  
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 ClarifyModelMonitor obj's underlying member method code with example usage: obj.run_baseline() without return types