self = 
shared_state = {'bucket': , 'resources': [}
@pytest.fixture(scope="class", autouse=True)
def teardown(self, shared_state: Dict[str, Any]):
    """Delete every Vertex AI resource created during test"""

    yield

    # Bring all Endpoints to the front of the list
    # Ensures Models are undeployed first before we attempt deletion
    shared_state["resources"].sort(
        key=lambda r: 1 if isinstance(r, aiplatform.Endpoint) else 2
    )

    for resource in shared_state["resources"]:
        try:
            if isinstance(resource, (aiplatform.Endpoint, aiplatform.Featurestore)):
                # For endpoint, undeploy model then delete endpoint
                # For featurestore, force delete its entity_types and features with the featurestore


              resource.delete(force=True)


tests/system/aiplatform/e2e_base.py:110:

google/cloud/aiplatform/models.py:1325: in delete

self.undeploy_all(sync=sync)

google/cloud/aiplatform/models.py:1297: in undeploy_all

self._sync_gca_resource()

google/cloud/aiplatform/base.py:601: in _sync_gca_resource

self._gca_resource = self._get_gca_resource(resource_name=self.resource_name)

google/cloud/aiplatform/base.py:612: in resource_name

self._assert_gca_resource_is_available()

google/cloud/aiplatform/models.py:164: in _assert_gca_resource_is_available

super()._assert_gca_resource_is_available()

self = <google.cloud.aiplatform.models.Endpoint object at 0x7f7277285940> failed with Training failed with:

code: 13

message: "Internal error occurred. Please retry in a few minutes. If you still experience errors, contact Vertex AI."
def _assert_gca_resource_is_available(self) -> None:
    """Helper method to raise when accessing properties that do not exist.

    Overrides VertexAiResourceNoun to provide a more informative exception if
    resource creation has failed asynchronously.

    Raises:
        RuntimeError: When resource has not been created.
    """
    if not getattr(self._gca_resource, "name", None):


      raise RuntimeError(


            f"{self.__class__.__name__} resource has not been created."
            + (
                f" Resource failed with: {self._exception}"
                if self._exception
                else ""
            )
        )

E           RuntimeError: Endpoint resource has not been created. Resource failed with: Training failed with:

E           code: 13

E           message: "Internal error occurred. Please retry in a few minutes. If you still experience errors, contact Vertex AI."
google/cloud/aiplatform/base.py:1202: RuntimeError