def METHOD_NAME(
    checkpoint: Checkpoint, config: EvaluationConfig, data_config: DataConfig
) -> EvaluationResults:
    """# Evaluate a model checkpoint for a ResNet model.
    ## Inputs
    - **checkpoint**: A checkpoint for a trained model
    - **config**: Configuration for compute for evaluation
    - **data_config**: Configuration for the data to be used during evaluation
    ## Output
    Summary of some statistics resulting from the evaluation.
    """
    cluster_config = SimpleRayCluster(
        n_nodes=config.n_workers, node_config=config.worker
    )
    checkpointer = SematicCheckpointIO(s3_location=checkpoint.prefix)
    # we want the driver for the eval to run on the
    # small ray cluster we're creating.
    @ray.remote(num_gpus=config.worker.gpu_count)
    def call_evaluate_classifier_from_ray():
        logging.basicConfig(level=logging.INFO)
        strategy_compute_kwargs = dict(
            num_workers=config.n_workers - 1,
            num_cpus_per_worker=config.worker.cpu,
            use_gpu=config.worker.gpu_count > 0,
        )
        return evaluate_classifier(
            checkpoint, config, data_config, strategy_compute_kwargs, checkpointer
        )
    with RayCluster(config=cluster_config):
        return ray.get(call_evaluate_classifier_from_ray.remote())