self = 
shared_state = {'bucket': , 'resources': [ 
resource name: projects/1065521786570/locations/us-central1/endpoints/647612348760064000]}
def test_upload_and_deploy_xgboost_model(self, shared_state):
    """Upload XGBoost model from local file and deploy it for prediction. Additionally, update model name, description and labels"""

    aiplatform.init(project=_TEST_PROJECT, location=_TEST_LOCATION)

    storage_client = storage.Client(project=_TEST_PROJECT)
    model_blob = storage.Blob.from_string(
        uri=_XGBOOST_MODEL_URI, client=storage_client
    )
    model_path = tempfile.mktemp() + ".my_model.xgb"
    model_blob.download_to_filename(filename=model_path)

    model = aiplatform.Model.upload_xgboost_model_file(model_file_path=model_path,)
    shared_state["resources"] = [model]

    staging_bucket = storage.Blob.from_string(
        uri=model.uri, client=storage_client
    ).bucket
    # Checking that the bucket is auto-generated
    assert "-vertex-staging-" in staging_bucket.name

    shared_state["bucket"] = staging_bucket

    # Currently we need to explicitly specify machine type.
    # See https://github.com/googleapis/python-aiplatform/issues/773
    endpoint = model.deploy(machine_type="n1-standard-2")
    shared_state["resources"].append(endpoint)
    predict_response = endpoint.predict(instances=[[0, 0, 0]])
    assert len(predict_response.predictions) == 1

    model = model.update(
        display_name="new_name",
        description="new_description",
        labels={"my_label": "updated"},
    )
    assert model.display_name == "new_name"


  assert model.display_name == "new_description"


E       AssertionError: assert 'new_name' == 'new_description'

E         - new_description

E         + new_name
tests/system/aiplatform/test_model_upload.py:75: AssertionError