def METHOD_NAME(
    self, client: MLClient, rand_batch_name: Callable[[], str], rand_batch_deployment_name: Callable[[], str]
) -> None:
    endpoint_yaml = "./tests/test_configs/endpoints/batch/batch_endpoint_deployment_component.yaml"
    endpoint_name = rand_batch_name("endpoint_name")
    endpoint = load_batch_endpoint(endpoint_yaml)
    endpoint.name = endpoint_name
    # Create deployment using local files
    deployment_yaml = "./tests/test_configs/deployments/batch/batch_deployment_component.yaml"
    deployment_name = rand_batch_deployment_name("deployment_name")
    deployment = load_batch_deployment(deployment_yaml)
    deployment.endpoint_name = endpoint_name
    deployment.name = deployment_name
    # create the batch endpoint
    endpoint = client.batch_endpoints.begin_create_or_update(endpoint).result()
    # create a deployment
    client.batch_deployments.begin_create_or_update(deployment).result()
    # Batch endpoint invoke using different supported inputs
    inputs_dict = {
        "input_1": Input(path="azureml:list_data_v2_test:2", type="uri_folder"),
        "input_2": Input(path="azureml:list_data_v2_test:2", type="uri_folder"),
    }
    job = client.batch_endpoints.invoke(
        endpoint_name=endpoint.name,
        deployment_name=deployment.name,
        inputs=inputs_dict,
    )
    assert job