class_docstr: Make prediction requests to an Amazon SageMaker endpoint.
class_docstr_tok: ["Make","prediction","requests","to","an","Amazon","SageMaker","endpoint","."]
docstr: List the deployed models co-located with this predictor.
  
  Calls SageMaker:ListInferenceComponents on the endpoint associated with
      the predictor.
  
  Args:
      variant_name_equals (str): Optional. A string that matches the name of the
          variant that was assigned to the inference component. (Default: None).
      name_contains (str): Optional. A string that partially matches the names of one or
          more inference components. Filters inference components by name.
          (Default: None).
      creation_time_after (datetime.datetime): Optional. Use this parameter to
          search for inference components created after a specific date and time.
          (Default: None).
      creation_time_before (datetime.datetime): Optional. Use this parameter to
          search for inference components created before a specific date and time.
          (Default: None).
      last_modified_time_after (datetime.datetime): Optional. Use this parameter to
          search for inference components that were last modified after a specific date
          and time. (Default: None).
      last_modified_time_before (datetime.datetime): Optional. Use this parameter to
          search for inference components that were last modified before a specific date
          and time. (Default: None).
      status_equals (str): Optional. The inference component status. Filters
          inference components by status. (Default: None).
      sort_order (str): Optional. The order in which inference components are listed.
          (Default: None).
      sort_order (str): Optional. The order in which inference components are listed in
          the response. (Default: None).
      max_results (int): Optional. The maximum number of results returned by
          list_related_models. (Default: None).
      next_token (str): Optional. A token to resume pagination of list_related_models
          results. (Default: None).
  
  Returns:
      Tuple[List[Dict[str, Any]], Optional[str]]: A list of Amazon SageMaker inference
          component objects associated with the endpoint. If a next token is returned,
          there are more results available. The value of the next token is a unique
          pagination token.
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code: def list_related_models(
      self,
      variant_name_equals: Optional[str] = None,
      name_contains: Optional[str] = None,
      creation_time_after: Optional[datetime.datetime] = None,
      creation_time_before: Optional[datetime.datetime] = None,
      last_modified_time_after: Optional[datetime.datetime] = None,
      last_modified_time_before: Optional[datetime.datetime] = None,
      status_equals: Optional[str] = None,
      sort_order: Optional[str] = None,
      sort_by: Optional[str] = None,
      max_results: Optional[int] = None,
      next_token: Optional[str] = None,
  ):
      """List the deployed models co-located with this predictor.
  
      Calls SageMaker:ListInferenceComponents on the endpoint associated with
          the predictor.
  
      Args:
          variant_name_equals (str): Optional. A string that matches the name of the
              variant that was assigned to the inference component. (Default: None).
          name_contains (str): Optional. A string that partially matches the names of one or
              more inference components. Filters inference components by name.
              (Default: None).
          creation_time_after (datetime.datetime): Optional. Use this parameter to
              search for inference components created after a specific date and time.
              (Default: None).
          creation_time_before (datetime.datetime): Optional. Use this parameter to
              search for inference components created before a specific date and time.
              (Default: None).
          last_modified_time_after (datetime.datetime): Optional. Use this parameter to
              search for inference components that were last modified after a specific date
              and time. (Default: None).
          last_modified_time_before (datetime.datetime): Optional. Use this parameter to
              search for inference components that were last modified before a specific date
              and time. (Default: None).
          status_equals (str): Optional. The inference component status. Filters
              inference components by status. (Default: None).
          sort_order (str): Optional. The order in which inference components are listed.
              (Default: None).
          sort_order (str): Optional. The order in which inference components are listed in
              the response. (Default: None).
          max_results (int): Optional. The maximum number of results returned by
              list_related_models. (Default: None).
          next_token (str): Optional. A token to resume pagination of list_related_models
              results. (Default: None).
  
      Returns:
          Tuple[List[Dict[str, Any]], Optional[str]]: A list of Amazon SageMaker inference
              component objects associated with the endpoint. If a next token is returned,
              there are more results available. The value of the next token is a unique
              pagination token.
      """
  
      response_dict = self.sagemaker_session.list_inference_components(
          endpoint_name_equals=self.endpoint_name,
          variant_name_equals=variant_name_equals,
          name_contains=name_contains,
          creation_time_after=creation_time_after,
          creation_time_before=creation_time_before,
          last_modified_time_after=last_modified_time_after,
          last_modified_time_before=last_modified_time_before,
          status_equals=status_equals,
          sort_order=sort_order,
          sort_by=sort_by,
          max_results=max_results,
          next_token=next_token,
      )
  
      if len(response_dict) == 0:
          LOGGER.info(
              "No deployed models found for endpoint %s.",
              self.endpoint_name,
          )
          return {}, None
  
      # parse list result if returned list inference components are not None
      inference_components = response_dict["InferenceComponents"]
      next_token_from_response = None
      if "NextToken" in response_dict:
          next_token_from_response = response_dict["NextToken"]
  
      return inference_components, next_token_from_response
  
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 10 for calling the Predictor obj's underlying member method code with example usage: obj.list_related_models(variant_name_equals, name_contains, creation_time_after, creation_time_before, last_modified_time_after, last_modified_time_before, status_equals, sort_order, sort_by, max_results, next_token) and returns: inference_components,  next_token_from_response, dict,  None