code: def test_uc_models_artifact_repo_with_stage_uri_raises():
      model_uri = "models:/MyModel/Staging"
      with mock.patch(
          "databricks_cli.configure.provider.get_config"
      ), pytest.raises(
          MlflowException,
          match="If seeing this error while attempting to load a model version by stage, note that "
          "setting stages and loading model versions by stage is unsupported in Unity Catalog",
      ):
          UnityCatalogModelsArtifactRepository(
              artifact_uri=model_uri,
              registry_uri=_DATABRICKS_UNITY_CATALOG_SCHEME,
          )
  
imports: import json
  from unittest import mock
  from unittest.mock import ANY
  import pytest
  from google.cloud.storage import Client
  from requests import Response
  from mlflow import MlflowClient
  from mlflow.entities.file_info import FileInfo
  from mlflow.exceptions import MlflowException
  from mlflow.store._unity_catalog.registry.utils import _ACTIVE_CATALOG_QUERY, _ACTIVE_SCHEMA_QUERY
  from mlflow.store.artifact.azure_data_lake_artifact_repo import AzureDataLakeArtifactRepository
  from mlflow.store.artifact.gcs_artifact_repo import GCSArtifactRepository
  from mlflow.store.artifact.optimized_s3_artifact_repo import OptimizedS3ArtifactRepository
  from mlflow.store.artifact.unity_catalog_models_artifact_repo import UnityCatalogModelsArtifactRepository
  from mlflow.utils.uri import _DATABRICKS_UNITY_CATALOG_SCHEME
label_desc: Use image node_id 4 for calling a global function with example usage: test_uc_models_artifact_repo_with_stage_uri_raises() without return types