class_docstr: Make async prediction requests to an Amazon SageMaker endpoint.
class_docstr_tok: ["Make","async","prediction","requests","to","an","Amazon","SageMaker","endpoint","."]
docstr: Generates ModelMonitor objects (or DefaultModelMonitors).
  
  Objects are generated based on the schedule(s) associated with the endpoint
  that this predictor refers to.
  
  Returns:
      [sagemaker.model_monitor.model_monitoring.ModelMonitor]: A list of
          ModelMonitor (or DefaultModelMonitor) objects.
docstr_tok: ["Generates","ModelMonitor","objects","(","or","DefaultModelMonitors",")",".","Objects","are","generated","based","on","the","schedule","(","s",")","associated","with","the","endpoint","that","this","predictor","refers","to",".","Returns",":","[","sagemaker.model_monitor.model_monitoring.ModelMonitor","]",":","A","list","of","ModelMonitor","(","or","DefaultModelMonitor",")","objects","."]
code: def list_monitors(self):
      """Generates ModelMonitor objects (or DefaultModelMonitors).
  
      Objects are generated based on the schedule(s) associated with the endpoint
      that this predictor refers to.
  
      Returns:
          [sagemaker.model_monitor.model_monitoring.ModelMonitor]: A list of
              ModelMonitor (or DefaultModelMonitor) objects.
  
      """
      return self.predictor.list_monitors()
  
imports: from __future__ import absolute_import
  import threading
  import time
  import uuid
  from botocore.exceptions import WaiterError
  from sagemaker import s3
  from sagemaker.exceptions import PollingTimeoutError, AsyncInferenceModelError
  from sagemaker.async_inference import WaiterConfig, AsyncInferenceResponse
  from sagemaker.s3 import parse_s3_url
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, sagemaker_timestamp, format_tags
label_desc: Use image node_id 16 for calling the AsyncPredictor obj's underlying member method code with example usage: obj.list_monitors() and returns: self