class_docstr: Amazon SageMaker model monitor to monitor feature attribution 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","feature","attribution","of","an","endpoint",".","Please","see","the","__init__","method","of","its","base","class","for","how","to","instantiate","it","."]
docstr: Deletes the monitoring schedule and its job definition.
code: def delete_monitoring_schedule(self):
      """Deletes the monitoring schedule and its job definition."""
      super(
          ModelExplainabilityMonitor, self
      ).delete_monitoring_schedule()
      # Delete job definition.
      message = "Deleting Model Explainability Job Definition with name: {}".format(
          self.job_definition_name
      )
      logger.info(message)
      self.sagemaker_session.sagemaker_client.delete_model_explainability_job_definition(
          JobDefinitionName=self.job_definition_name
      )
      self.job_definition_name = None
  
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 5 for calling the ModelExplainabilityMonitor obj's underlying member method code with example usage: obj.delete_monitoring_schedule() without return types