code: def get_feature_dependencies(model_dir):
      """
      Gets the features which a model depends on. This functionality is only implemented on
      Databricks. In OSS mlflow, the dependencies are always empty ("").
      """
      model = _load_model(model_dir)
      model_info = model.get_model_info()
      if (
          model_info.flavors.get("python_function", {}).get(
              "loader_module"
          )
          == mlflow.models.model._DATABRICKS_FS_LOADER_MODULE
      ):
          raise MlflowException(
              "This model was packaged by Databricks Feature Store and can only be registered on a "
              "Databricks cluster."
          )
      return ""
  
imports: import base64
  import functools
  import logging
  import os
  import shutil
  from contextlib import contextmanager
  import mlflow
  from mlflow.entities import Run
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_uc_registry_messages_pb2 import MODEL_VERSION_OPERATION_READ_WRITE, CreateModelVersionRequest, CreateModelVersionResponse, CreateRegisteredModelRequest, CreateRegisteredModelResponse, DeleteModelVersionRequest, DeleteModelVersionResponse, DeleteModelVersionTagRequest, DeleteModelVersionTagResponse, DeleteRegisteredModelAliasRequest, DeleteRegisteredModelAliasResponse, DeleteRegisteredModelRequest, DeleteRegisteredModelResponse, DeleteRegisteredModelTagRequest, DeleteRegisteredModelTagResponse, Entity, FinalizeModelVersionRequest, FinalizeModelVersionResponse, GenerateTemporaryModelVersionCredentialsRequest, GenerateTemporaryModelVersionCredentialsResponse, GetModelVersionByAliasRequest, GetModelVersionByAliasResponse, GetModelVersionDownloadUriRequest, GetModelVersionDownloadUriResponse, GetModelVersionRequest, GetModelVersionResponse, GetRegisteredModelRequest, GetRegisteredModelResponse, Job, LineageHeaderInfo, Notebook, SearchModelVersionsRequest, SearchModelVersionsResponse, SearchRegisteredModelsRequest, SearchRegisteredModelsResponse, SetModelVersionTagRequest, SetModelVersionTagResponse, SetRegisteredModelAliasRequest, SetRegisteredModelAliasResponse, SetRegisteredModelTagRequest, SetRegisteredModelTagResponse, TemporaryCredentials, UpdateModelVersionRequest, UpdateModelVersionResponse, UpdateRegisteredModelRequest, UpdateRegisteredModelResponse
  from mlflow.protos.databricks_uc_registry_service_pb2 import UcModelRegistryService
  from mlflow.protos.service_pb2 import GetRun, MlflowService
  from mlflow.store._unity_catalog.registry.utils import get_artifact_repo_from_storage_info, get_full_name_from_sc, model_version_from_uc_proto, registered_model_from_uc_proto, uc_model_version_tag_from_mlflow_tags, uc_registered_model_tag_from_mlflow_tags
  from mlflow.store.entities.paged_list import PagedList
  from mlflow.store.model_registry.rest_store import BaseRestStore
  from mlflow.utils._spark_utils import _get_active_spark_session
  from mlflow.utils.annotations import experimental
  from mlflow.utils.databricks_utils import get_databricks_host_creds, is_databricks_uri
  from mlflow.utils.mlflow_tags import MLFLOW_DATABRICKS_JOB_ID, MLFLOW_DATABRICKS_JOB_RUN_ID, MLFLOW_DATABRICKS_NOTEBOOK_ID
  from mlflow.utils.proto_json_utils import message_to_json, parse_dict
  from mlflow.utils.rest_utils import _REST_API_PATH_PREFIX, extract_all_api_info_for_service, extract_api_info_for_service, http_request, verify_rest_response
label_desc: Use image node_id 5 for calling a global function with example usage: get_feature_dependencies(model_dir) and returns: str