class_docstr: Make prediction requests to an Amazon SageMaker endpoint.
class_docstr_tok: ["Make","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.
  
      """
      monitoring_schedules_dict = (
          self.sagemaker_session.list_monitoring_schedules(
              endpoint_name=self.endpoint_name
          )
      )
      if (
          len(monitoring_schedules_dict["MonitoringScheduleSummaries"])
          == 0
      ):
          logger.debug(
              "No monitors found for endpoint. endpoint: %s",
              self.endpoint_name,
          )
          return []
  
      monitors = []
      for schedule_dict in monitoring_schedules_dict[
          "MonitoringScheduleSummaries"
      ]:
          schedule_name = schedule_dict["MonitoringScheduleName"]
          monitoring_type = schedule_dict.get("MonitoringType")
          clazz = self._get_model_monitor_class(
              schedule_name, monitoring_type
          )
          monitors.append(
              clazz.attach(
                  monitor_schedule_name=schedule_name,
                  sagemaker_session=self.sagemaker_session,
              )
          )
  
      return monitors
  
imports: from __future__ import print_function, absolute_import
  import abc
  import datetime
  from typing import Any, Dict, Optional, Tuple, Union
  import logging
  from sagemaker.enums import EndpointType
  from sagemaker.deprecations import deprecated_class, deprecated_deserialize, deprecated_serialize, removed_kwargs, renamed_kwargs, renamed_warning
  from sagemaker.deserializers import BytesDeserializer, CSVDeserializer, JSONDeserializer, NumpyDeserializer, StreamDeserializer, StringDeserializer
  from sagemaker.jumpstart.payload_utils import PayloadSerializer
  from sagemaker.jumpstart.types import JumpStartSerializablePayload
  from sagemaker.jumpstart.utils import get_jumpstart_content_bucket
  from sagemaker.model_monitor import DataCaptureConfig, DefaultModelMonitor, ModelBiasMonitor, ModelExplainabilityMonitor, ModelMonitor, ModelQualityMonitor
  from sagemaker.serializers import CSVSerializer, IdentitySerializer, JSONSerializer, NumpySerializer
  from sagemaker.session import production_variant, Session
  from sagemaker.utils import name_from_base, stringify_object, format_tags
  from sagemaker.model_monitor.model_monitoring import DEFAULT_REPOSITORY_NAME
  from sagemaker.lineage.context import EndpointContext
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
label_desc: Use image node_id 15 for calling the Predictor obj's underlying member method code with example usage: obj.list_monitors() and returns: monitors, list