code: def retrieve(
      framework,
      region,
      version=None,
      py_version=None,
      instance_type=None,
      accelerator_type=None,
      image_scope=None,
      container_version=None,
      distribution=None,
      base_framework_version=None,
      training_compiler_config=None,
      model_id=None,
      model_version=None,
      tolerate_vulnerable_model=False,
      tolerate_deprecated_model=False,
      sdk_version=None,
      inference_tool=None,
      serverless_inference_config=None,
      sagemaker_session=DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
  ) -> str:
      """Retrieves the ECR URI for the Docker image matching the given arguments.
  
      Ideally this function should not be called directly, rather it should be called from the
      fit() function inside framework estimator.
  
      Args:
          framework (str): The name of the framework or algorithm.
          region (str): The AWS region.
          version (str): The framework or algorithm version. This is required if there is
              more than one supported version for the given framework or algorithm.
          py_version (str): The Python version. This is required if there is
              more than one supported Python version for the given framework version.
          instance_type (str): The SageMaker instance type. For supported types, see
              https://aws.amazon.com/sagemaker/pricing. This is required if
              there are different images for different processor types.
          accelerator_type (str): Elastic Inference accelerator type. For more, see
              https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html.
          image_scope (str): The image type, i.e. what it is used for.
              Valid values: "training", "inference", "inference_graviton", "eia".
              If ``accelerator_type`` is set, ``image_scope`` is ignored.
          container_version (str): the version of docker image.
              Ideally the value of parameter should be created inside the framework.
              For custom use, see the list of supported container versions:
              https://github.com/aws/deep-learning-containers/blob/master/available_images.md
              (default: None).
          distribution (dict): A dictionary with information on how to run distributed training
          training_compiler_config (:class:`~sagemaker.training_compiler.TrainingCompilerConfig`):
              A configuration class for the SageMaker Training Compiler
              (default: None).
          model_id (str): The JumpStart model ID for which to retrieve the image URI
              (default: None).
          model_version (str): The version of the JumpStart model for which to retrieve the
              image URI (default: None).
          tolerate_vulnerable_model (bool): ``True`` if vulnerable versions of model specifications
              should be tolerated without an exception 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 versions of model specifications
              should be tolerated without an exception raised. If False, raises an exception
              if the version of the model is deprecated. (Default: False).
          sdk_version (str): the version of python-sdk that will be used in the image retrieval.
              (default: None).
          inference_tool (str): the tool that will be used to aid in the inference.
              Valid values: "neuron, neuronx, None"
              (default: None).
          serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
              Specifies configuration related to serverless endpoint. Instance type is
              not provided in serverless inference. So this is used to determine processor type.
          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).
  
      Returns:
          str: The ECR URI for the corresponding SageMaker Docker image.
  
      Raises:
          NotImplementedError: If the scope is not supported.
          ValueError: If the combination of arguments specified is not supported or
              any PipelineVariable object is passed in.
          VulnerableJumpStartModelError: If any of the dependencies required by the script have
              known security vulnerabilities.
          DeprecatedJumpStartModelError: If the version of the model is deprecated.
      """
      args = dict(locals())
      for name, val in args.items():
          if is_pipeline_variable(val):
              raise ValueError(
                  "When retrieving the image_uri, the argument %s should not be a pipeline variable "
                  "(%s) since pipeline variables are only interpreted in the pipeline execution time."
                  % (name, type(val))
              )
  
      if is_jumpstart_model_input(model_id, model_version):
          return artifacts._retrieve_image_uri(
              model_id,
              model_version,
              image_scope,
              framework,
              region,
              version,
              py_version,
              instance_type,
              accelerator_type,
              container_version,
              distribution,
              base_framework_version,
              training_compiler_config,
              tolerate_vulnerable_model,
              tolerate_deprecated_model,
              sagemaker_session=sagemaker_session,
          )
  
      if training_compiler_config and (
          framework in [HUGGING_FACE_FRAMEWORK, "pytorch"]
      ):
          final_image_scope = image_scope
          config = _config_for_framework_and_scope(
              framework + "-training-compiler",
              final_image_scope,
              accelerator_type,
          )
      else:
          _framework = framework
          if (
              framework == HUGGING_FACE_FRAMEWORK
              or framework in TRAINIUM_ALLOWED_FRAMEWORKS
          ):
              inference_tool = _get_inference_tool(
                  inference_tool, instance_type
              )
              if inference_tool in ["neuron", "neuronx"]:
                  _framework = f"{framework}-{inference_tool}"
          final_image_scope = _get_final_image_scope(
              framework, instance_type, image_scope
          )
          _validate_for_suppported_frameworks_and_instance_type(
              framework, instance_type
          )
          config = _config_for_framework_and_scope(
              _framework, final_image_scope, accelerator_type
          )
  
      original_version = version
      version = _validate_version_and_set_if_needed(
          version, config, framework
      )
      version_config = config["versions"][
          _version_for_config(version, config)
      ]
  
      if framework == HUGGING_FACE_FRAMEWORK:
          if version_config.get("version_aliases"):
              full_base_framework_version = version_config[
                  "version_aliases"
              ].get(base_framework_version, base_framework_version)
          _validate_arg(
              full_base_framework_version,
              list(version_config.keys()),
              "base framework",
          )
          version_config = version_config.get(
              full_base_framework_version
          )
  
      py_version = _validate_py_version_and_set_if_needed(
          py_version, version_config, framework
      )
      version_config = version_config.get(py_version) or version_config
      registry = _registry_from_region(
          region, version_config["registries"]
      )
      endpoint_data = utils._botocore_resolver().construct_endpoint(
          "ecr", region
      )
      if region == "il-central-1" and not endpoint_data:
          endpoint_data = {
              "hostname": "ecr.{}.amazonaws.com".format(region)
          }
      hostname = endpoint_data["hostname"]
  
      repo = version_config["repository"]
  
      processor = _processor(
          instance_type,
          config.get("processors") or version_config.get("processors"),
          serverless_inference_config,
      )
  
      # if container version is available in .json file, utilize that
      if version_config.get("container_version"):
          container_version = version_config["container_version"][
              processor
          ]
  
      # Append sdk version in case of trainium instances
      if repo in ["pytorch-training-neuron"]:
          if not sdk_version:
              sdk_version = _get_latest_versions(
                  version_config["sdk_versions"]
              )
          container_version = sdk_version + "-" + container_version
  
      if framework == HUGGING_FACE_FRAMEWORK:
          pt_or_tf_version = (
              re.compile("^(pytorch|tensorflow)(.*)$")
              .match(base_framework_version)
              .group(2)
          )
          _version = original_version
  
          if repo in [
              "huggingface-pytorch-trcomp-training",
              "huggingface-tensorflow-trcomp-training",
          ]:
              _version = version
          if repo in [
              "huggingface-pytorch-inference-neuron",
              "huggingface-pytorch-inference-neuronx",
          ]:
              if not sdk_version:
                  sdk_version = _get_latest_versions(
                      version_config["sdk_versions"]
                  )
              container_version = sdk_version + "-" + container_version
              if config.get("version_aliases").get(original_version):
                  _version = config.get("version_aliases")[
                      original_version
                  ]
              if (
                  config.get("versions", {})
                  .get(_version, {})
                  .get("version_aliases", {})
                  .get(base_framework_version, {})
              ):
                  _base_framework_version = config.get("versions")[
                      _version
                  ]["version_aliases"][base_framework_version]
                  pt_or_tf_version = (
                      re.compile("^(pytorch|tensorflow)(.*)$")
                      .match(_base_framework_version)
                      .group(2)
                  )
  
          tag_prefix = f"{pt_or_tf_version}-transformers{_version}"
      else:
          tag_prefix = version_config.get("tag_prefix", version)
  
      if repo == f"{framework}-inference-graviton":
          container_version = f"{container_version}-sagemaker"
      _validate_instance_deprecation(framework, instance_type, version)
  
      tag = _get_image_tag(
          container_version,
          distribution,
          final_image_scope,
          framework,
          inference_tool,
          instance_type,
          processor,
          py_version,
          tag_prefix,
          version,
      )
  
      if tag:
          repo += ":{}".format(tag)
  
      return ECR_URI_TEMPLATE.format(
          registry=registry, hostname=hostname, repository=repo
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import re
  from typing import Optional
  from packaging.version import Version
  from sagemaker import utils
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
  from sagemaker.jumpstart.utils import is_jumpstart_model_input
  from sagemaker.spark import defaults
  from sagemaker.jumpstart import artifacts
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.utilities import override_pipeline_parameter_var
  from sagemaker.fw_utils import GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY, GRAVITON_ALLOWED_FRAMEWORKS
label_desc: Use image node_id 1 for calling a global function with example usage: retrieve(framework, region, version, py_version, instance_type, accelerator_type, image_scope, container_version, distribution, base_framework_version, training_compiler_config, model_id, model_version, tolerate_vulnerable_model, tolerate_deprecated_model, sdk_version, inference_tool, serverless_inference_config, sagemaker_session) and returns: ECR_URI_TEMPLATE, artifacts