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: Sets this object's schedule name to the name provided.
  
  This allows subsequent describe_schedule or list_executions calls to point
  to the given schedule.
  
  Args:
      monitor_schedule_name (str): The name of the schedule to attach to.
      sagemaker_session (sagemaker.session.Session): Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed. If not specified, one is created using
          the default AWS configuration chain.
code: def attach(cls, monitor_schedule_name, sagemaker_session=None):
      """Sets this object's schedule name to the name provided.
  
      This allows subsequent describe_schedule or list_executions calls to point
      to the given schedule.
  
      Args:
          monitor_schedule_name (str): The name of the schedule to attach to.
          sagemaker_session (sagemaker.session.Session): Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed. If not specified, one is created using
              the default AWS configuration chain.
      """
      sagemaker_session = sagemaker_session or Session()
      schedule_desc = sagemaker_session.describe_monitoring_schedule(
          monitoring_schedule_name=monitor_schedule_name
      )
      monitoring_type = schedule_desc["MonitoringScheduleConfig"].get(
          "MonitoringType"
      )
      if monitoring_type != cls.monitoring_type():
          raise TypeError(
              "{} can only attach to ModelExplainability schedule.".format(
                  __class__.__name__
              )
          )
      job_definition_name = schedule_desc["MonitoringScheduleConfig"][
          "MonitoringJobDefinitionName"
      ]
      job_desc = sagemaker_session.sagemaker_client.describe_model_explainability_job_definition(
          JobDefinitionName=job_definition_name
      )
      tags = sagemaker_session.list_tags(
          resource_arn=schedule_desc["MonitoringScheduleArn"]
      )
      return ClarifyModelMonitor._attach(
          clazz=cls,
          sagemaker_session=sagemaker_session,
          schedule_desc=schedule_desc,
          job_desc=job_desc,
          tags=tags,
      )
  
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 6 for calling the ModelExplainabilityMonitor obj's underlying member method code with example usage: obj.attach(cls, monitor_schedule_name, sagemaker_session) and returns: ClarifyModelMonitor