code: def verify_model_region_and_return_specs(
      model_id: Optional[str],
      version: Optional[str],
      scope: Optional[str],
      region: str,
      tolerate_vulnerable_model: bool = False,
      tolerate_deprecated_model: bool = False,
      sagemaker_session: Session = constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
  ) -> JumpStartModelSpecs:
      """Verifies that an acceptable model_id, version, scope, and region combination is provided.
  
      Args:
          model_id (Optional[str]): model ID of the JumpStart model to verify and
              obtains specs.
          version (Optional[str]): version of the JumpStart model to verify and
              obtains specs.
          scope (Optional[str]): scope of the JumpStart model to verify.
          region (Optional[str]): region of the JumpStart model to verify and
              obtains specs.
          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).
  
      Raises:
          NotImplementedError: If the scope is not supported.
          ValueError: If the combination of arguments specified is not supported.
          VulnerableJumpStartModelError: If any of the dependencies required by the script have
              known security vulnerabilities.
          DeprecatedJumpStartModelError: If the version of the model is deprecated.
      """
  
      if scope is None:
          raise ValueError(
              "Must specify `model_scope` argument to retrieve model "
              "artifact uri for JumpStart models."
          )
  
      if scope not in constants.SUPPORTED_JUMPSTART_SCOPES:
          raise NotImplementedError(
              "JumpStart models only support scopes: "
              f"{', '.join(constants.SUPPORTED_JUMPSTART_SCOPES)}."
          )
  
      model_specs = accessors.JumpStartModelsAccessor.get_model_specs(  # type: ignore
          region=region,
          model_id=model_id,
          version=version,
          s3_client=sagemaker_session.s3_client,
      )
  
      if (
          scope == constants.JumpStartScriptScope.TRAINING.value
          and not model_specs.training_supported
      ):
          raise ValueError(
              f"JumpStart model ID '{model_id}' and version '{version}' "
              "does not support training."
          )
  
      if model_specs.deprecated:
          if not tolerate_deprecated_model:
              raise DeprecatedJumpStartModelError(
                  model_id=model_id,
                  version=version,
                  message=model_specs.deprecated_message,
              )
  
      if (
          scope == constants.JumpStartScriptScope.INFERENCE.value
          and model_specs.inference_vulnerable
      ):
          if not tolerate_vulnerable_model:
              raise VulnerableJumpStartModelError(
                  model_id=model_id,
                  version=version,
                  vulnerabilities=model_specs.inference_vulnerabilities,
                  scope=constants.JumpStartScriptScope.INFERENCE,
              )
  
      if (
          scope == constants.JumpStartScriptScope.TRAINING.value
          and model_specs.training_vulnerable
      ):
          if not tolerate_vulnerable_model:
              raise VulnerableJumpStartModelError(
                  model_id=model_id,
                  version=version,
                  vulnerabilities=model_specs.training_vulnerabilities,
                  scope=constants.JumpStartScriptScope.TRAINING,
              )
  
      return model_specs
  
imports: from __future__ import absolute_import
  import logging
  import os
  from typing import Any, Dict, List, Optional, Union
  from urllib.parse import urlparse
  import boto3
  from packaging.version import Version
  import sagemaker
  from sagemaker.config.config_schema import MODEL_ENABLE_NETWORK_ISOLATION_PATH, MODEL_EXECUTION_ROLE_ARN_PATH, TRAINING_JOB_ENABLE_NETWORK_ISOLATION_PATH, TRAINING_JOB_INTER_CONTAINER_ENCRYPTION_PATH, TRAINING_JOB_ROLE_ARN_PATH
  from sagemaker.jumpstart import constants, enums
  from sagemaker.jumpstart import accessors
  from sagemaker.s3 import parse_s3_url
  from sagemaker.jumpstart.exceptions import DeprecatedJumpStartModelError, VulnerableJumpStartModelError, get_old_model_version_msg
  from sagemaker.jumpstart.types import JumpStartModelHeader, JumpStartModelSpecs, JumpStartVersionedModelId
  from sagemaker.session import Session
  from sagemaker.config import load_sagemaker_config
  from sagemaker.utils import resolve_value_from_config, TagsDict
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 17 for calling a global function with example usage: verify_model_region_and_return_specs(model_id, version, scope, region, tolerate_vulnerable_model, tolerate_deprecated_model, sagemaker_session) and returns: model_specs