class_docstr: JumpStartEstimator class.
  
  This class sets defaults based on the model ID and version.
docstr: Attach to an existing training job.
  
  Create a JumpStartEstimator bound to an existing training job.
  After attaching, if the training job has a Complete status,
  it can be ``deploy()`` ed to create a SageMaker Endpoint and return
  a ``Predictor``.
  
  If the training job is in progress, attach will block until the training job
  completes, but logs of the training job will not display. To see the logs
  content, please call ``logs()``
  
  Examples:
      >>> my_estimator.fit(wait=False)
      >>> training_job_name = my_estimator.latest_training_job.name
      Later on:
      >>> attached_estimator = JumpStartEstimator.attach(training_job_name, model_id)
      >>> attached_estimator.logs()
      >>> attached_estimator.deploy()
  
  Args:
      training_job_name (str): The name of the training job to attach to.
      model_id (str): The name of the JumpStart model id associated with the
          training job.
      model_version (str): Optional. The version of the JumpStart model id
          associated with the training job. (Default: "*").
      sagemaker_session (sagemaker.session.Session): Optional. Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed. If not specified, the estimator creates one
          using the default AWS configuration chain.
          (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
      model_channel_name (str): Optional. Name of the channel where pre-trained
          model data will be downloaded (default: 'model'). If no channel
          with the same name exists in the training job, this option will
          be ignored.
  
  Returns:
      Instance of the calling ``JumpStartEstimator`` Class with the attached
      training job.
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code: def attach(
      cls,
      training_job_name: str,
      model_id: str,
      model_version: str = "*",
      sagemaker_session: session.Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
      model_channel_name: str = "model",
  ) -> "JumpStartEstimator":
      """Attach to an existing training job.
  
      Create a JumpStartEstimator bound to an existing training job.
      After attaching, if the training job has a Complete status,
      it can be ``deploy()`` ed to create a SageMaker Endpoint and return
      a ``Predictor``.
  
      If the training job is in progress, attach will block until the training job
      completes, but logs of the training job will not display. To see the logs
      content, please call ``logs()``
  
      Examples:
          >>> my_estimator.fit(wait=False)
          >>> training_job_name = my_estimator.latest_training_job.name
          Later on:
          >>> attached_estimator = JumpStartEstimator.attach(training_job_name, model_id)
          >>> attached_estimator.logs()
          >>> attached_estimator.deploy()
  
      Args:
          training_job_name (str): The name of the training job to attach to.
          model_id (str): The name of the JumpStart model id associated with the
              training job.
          model_version (str): Optional. The version of the JumpStart model id
              associated with the training job. (Default: "*").
          sagemaker_session (sagemaker.session.Session): Optional. Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed. If not specified, the estimator creates one
              using the default AWS configuration chain.
              (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
          model_channel_name (str): Optional. Name of the channel where pre-trained
              model data will be downloaded (default: 'model'). If no channel
              with the same name exists in the training job, this option will
              be ignored.
  
      Returns:
          Instance of the calling ``JumpStartEstimator`` Class with the attached
          training job.
      """
  
      return cls._attach(
          training_job_name=training_job_name,
          sagemaker_session=sagemaker_session,
          model_channel_name=model_channel_name,
          additional_kwargs={
              "model_id": model_id,
              "model_version": model_version,
          },
      )
  
imports: from __future__ import absolute_import
  from typing import Dict, List, Optional, Union
  from sagemaker import session
  from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
  from sagemaker.base_deserializers import BaseDeserializer
  from sagemaker.base_serializers import BaseSerializer
  from sagemaker.debugger.debugger import DebuggerHookConfig, RuleBase, TensorBoardOutputConfig
  from sagemaker.debugger.profiler_config import ProfilerConfig
  from sagemaker.estimator import Estimator
  from sagemaker.explainer.explainer_config import ExplainerConfig
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.jumpstart.accessors import JumpStartModelsAccessor
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
  from sagemaker.jumpstart.enums import JumpStartScriptScope
  from sagemaker.jumpstart.exceptions import INVALID_MODEL_ID_ERROR_MSG
  from sagemaker.jumpstart.factory.estimator import get_deploy_kwargs, get_fit_kwargs, get_init_kwargs
  from sagemaker.jumpstart.factory.model import get_default_predictor
  from sagemaker.jumpstart.utils import is_valid_model_id, resolve_model_sagemaker_config_field
  from sagemaker.utils import stringify_object, format_tags, Tags
  from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
  from sagemaker.predictor import PredictorBase
  from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 3 for calling the JumpStartEstimator obj's underlying member method code with example usage: obj.attach(cls, training_job_name, model_id, model_version, sagemaker_session, model_channel_name) and returns: cls