code: def get_default_predictor(
      predictor: Predictor,
      model_id: str,
      model_version: str,
      region: str,
      tolerate_vulnerable_model: bool,
      tolerate_deprecated_model: bool,
      sagemaker_session: Session,
  ) -> Predictor:
      """Converts predictor returned from ``Model.deploy()`` into a JumpStart-specific one.
  
      Raises:
          RuntimeError: If a base-class predictor is not used.
      """
  
      # if there's a non-default predictor, do not mutate -- return as is
      if type(predictor) != Predictor:  # pylint: disable=C0123
          raise RuntimeError(
              "Can only get default predictor from base Predictor class. "
              f"Using Predictor class '{type(predictor).__name__}'."
          )
  
      predictor.serializer = serializers.retrieve_default(
          model_id=model_id,
          model_version=model_version,
          region=region,
          tolerate_deprecated_model=tolerate_deprecated_model,
          tolerate_vulnerable_model=tolerate_vulnerable_model,
          sagemaker_session=sagemaker_session,
      )
      predictor.deserializer = deserializers.retrieve_default(
          model_id=model_id,
          model_version=model_version,
          region=region,
          tolerate_deprecated_model=tolerate_deprecated_model,
          tolerate_vulnerable_model=tolerate_vulnerable_model,
          sagemaker_session=sagemaker_session,
      )
      predictor.accept = accept_types.retrieve_default(
          model_id=model_id,
          model_version=model_version,
          region=region,
          tolerate_deprecated_model=tolerate_deprecated_model,
          tolerate_vulnerable_model=tolerate_vulnerable_model,
          sagemaker_session=sagemaker_session,
      )
      predictor.content_type = content_types.retrieve_default(
          model_id=model_id,
          model_version=model_version,
          region=region,
          tolerate_deprecated_model=tolerate_deprecated_model,
          tolerate_vulnerable_model=tolerate_vulnerable_model,
          sagemaker_session=sagemaker_session,
      )
  
      return predictor
  
imports: from __future__ import absolute_import
  import json
  from typing import Any, Dict, List, Optional, Union
  from sagemaker import environment_variables, image_uris, instance_types, model_uris, script_uris
  from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
  from sagemaker.base_deserializers import BaseDeserializer
  from sagemaker.base_serializers import BaseSerializer
  from sagemaker.explainer.explainer_config import ExplainerConfig
  from sagemaker.jumpstart.artifacts import _model_supports_inference_script_uri, _retrieve_model_init_kwargs, _retrieve_model_deploy_kwargs, _retrieve_model_package_arn
  from sagemaker.jumpstart.artifacts.resource_names import _retrieve_resource_name_base
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION, INFERENCE_ENTRY_POINT_SCRIPT_NAME, JUMPSTART_DEFAULT_REGION_NAME, JUMPSTART_LOGGER
  from sagemaker.model_metrics import ModelMetrics
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.jumpstart.enums import JumpStartScriptScope
  from sagemaker.jumpstart.types import JumpStartModelDeployKwargs, JumpStartModelInitKwargs, JumpStartModelRegisterKwargs
  from sagemaker.jumpstart.utils import add_jumpstart_model_id_version_tags, update_dict_if_key_not_present, resolve_model_sagemaker_config_field, verify_model_region_and_return_specs
  from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
  from sagemaker.base_predictor import Predictor
  from sagemaker import accept_types, content_types, serializers, deserializers
  from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, format_tags, Tags
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
  from sagemaker import resource_requirements
  from sagemaker.enums import EndpointType
label_desc: Use image node_id 1 for calling a global function with example usage: get_default_predictor(predictor, model_id, model_version, region, tolerate_vulnerable_model, tolerate_deprecated_model, sagemaker_session) and returns: predictor