code: def get_special_model_spec_for_inference_component_based_endpoint(
      region: str = None,
      model_id: str = None,
      version: str = None,
      s3_client: boto3.client = None,
  ) -> JumpStartModelSpecs:
      """This function mocks cache accessor functions. For this mock,
      we only retrieve model specs based on the model ID and adding
      inference component based endpoint specific specification.
      This is reserved for special specs.
      """
      model_spec_dict = SPECIAL_MODEL_SPECS_DICT[model_id]
      model_spec_dict["hosting_resource_requirements"] = {
          "num_accelerators": 1,
          "min_memory_mb": 34360,
      }
      model_spec_dict["dynamic_container_deployment_supported"] = True
      specs = JumpStartModelSpecs(model_spec_dict)
      return specs
  
file_path: sagemaker-python-sdk/tests/unit/sagemaker/jumpstart/utils.py
imports: from __future__ import absolute_import
  import copy
  from typing import List
  import boto3
  from sagemaker.jumpstart.cache import JumpStartModelsCache
  from sagemaker.jumpstart.constants import JUMPSTART_DEFAULT_REGION_NAME, JUMPSTART_LOGGER, JUMPSTART_REGION_NAME_SET
  from sagemaker.jumpstart.types import JumpStartCachedS3ContentKey, JumpStartCachedS3ContentValue, JumpStartModelSpecs, JumpStartS3FileType, JumpStartModelHeader
  from sagemaker.jumpstart.utils import get_formatted_manifest
  from tests.unit.sagemaker.jumpstart.constants import PROTOTYPICAL_MODEL_SPECS_DICT, BASE_MANIFEST, BASE_SPEC, BASE_HEADER, SPECIAL_MODEL_SPECS_DICT
label_desc: Use image node_id 5 for calling a global function with example usage: get_special_model_spec_for_inference_component_based_endpoint(region, model_id, version, s3_client) and returns: specs