code: def retrieve(
      region: Optional[str] = None,
      model_id: Optional[str] = None,
      model_version: Optional[str] = None,
      scope: Optional[str] = None,
      tolerate_vulnerable_model: bool = False,
      tolerate_deprecated_model: bool = False,
      sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
      training_instance_type: Optional[str] = None,
  ) -> List[str]:
      """Retrieves the supported training instance types for the model matching the given arguments.
  
      Args:
          region (str): The AWS Region for which to retrieve the supported instance types.
              Defaults to ``None``.
          model_id (str): The model ID of the model for which to
              retrieve the supported instance types. (Default: None).
          model_version (str): The version of the model for which to retrieve the
              supported instance types. (Default: None).
          tolerate_vulnerable_model (bool): True if vulnerable versions of model
              specifications should be tolerated (exception not raised). If False, raises an
              exception if the script used by this version of the model has dependencies with known
              security vulnerabilities. (Default: False).
          tolerate_deprecated_model (bool): True if deprecated models should be tolerated
              (exception not raised). False if these models should raise an exception.
              (Default: False).
          sagemaker_session (sagemaker.session.Session): A SageMaker Session
              object, used for SageMaker interactions. If not
              specified, one is created using the default AWS configuration
              chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
          training_instance_type (str): In the case of a model fine-tuned on SageMaker, the training
              instance type used for the training job that produced the fine-tuned weights.
              Optionally supply this to get a inference instance type conditioned
              on the training instance, to ensure compatability of training artifact to inference
              instance. (Default: None).
  
      Returns:
          list: The supported instance types to use for the model.
  
      Raises:
          ValueError: If the combination of arguments specified is not supported.
      """
      if not jumpstart_utils.is_jumpstart_model_input(
          model_id, model_version
      ):
          raise ValueError(
              "Must specify JumpStart `model_id` and `model_version` when retrieving instance types."
          )
  
      if scope is None:
          raise ValueError("Must specify scope for instance types.")
  
      return artifacts._retrieve_instance_types(
          model_id,
          model_version,
          scope,
          region,
          tolerate_vulnerable_model,
          tolerate_deprecated_model,
          sagemaker_session=sagemaker_session,
          training_instance_type=training_instance_type,
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/instance_types.py
imports: from __future__ import absolute_import
  import logging
  from typing import List, Optional
  from sagemaker.jumpstart import utils
  from sagemaker.jumpstart import artifacts
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
  from sagemaker.session import Session
label_desc: Use image node_id 2 for calling a global function with example usage: retrieve(region, model_id, model_version, scope, tolerate_vulnerable_model, tolerate_deprecated_model, sagemaker_session, training_instance_type) and returns: artifacts