def METHOD_NAME():
    try:
        import poptorch
        ipu_opts, ipu_inference_opts = _get_ipu_opts(CONFIG_EXTRACT)
        # Define the expected IPU options for comparison
        expected_ipu_opts = [
            "deviceIterations(5)",
            "replicationFactor(16)",
            "TensorLocations.numIOTiles(128)",
            '_Popart.set("defaultBufferingDepth", 128)',
            "Precision.enableStochasticRounding(True)",
        ]
        expected_ipu_inference_opts = [
            "deviceIterations(1)",
            "replicationFactor(4)",
            "TensorLocations.numIOTiles(32)",
            '_Popart.set("defaultBufferingDepth", 16)',
            "Precision.enableStochasticRounding(True)",
        ]
        # Test the _get_ipu_opts method
        ipu_opts, ipu_inference_opts = _get_ipu_opts(CONFIG_EXTRACT)
        assert ipu_opts == expected_ipu_opts, f"Expected {expected_ipu_opts}, but got {ipu_opts}"
        assert (
            ipu_inference_opts == expected_ipu_inference_opts
        ), f"Expected {expected_ipu_inference_opts}, but got {ipu_inference_opts}"
        # Test the load_ipu_options method
        ipu_training_opts, ipu_inference_opts = load_ipu_options(
            ipu_opts=ipu_opts,
            seed=42,
            model_name="test_model",
            gradient_accumulation=CONFIG_EXTRACT["trainer"]["trainer"].get("accumulate_grad_batches", None),
            ipu_inference_opts=ipu_inference_opts,
        )
        # Ensure that the options objects are not None
        assert ipu_training_opts is not None, "Expected ipu_training_opts not to be None"
        assert ipu_inference_opts is not None, "Expected ipu_inference_opts not to be None"
        # Test the properties of the options objects
        assert (
            ipu_training_opts.replication_factor == 16
        ), "Expected replication_factor of ipu_training_opts to be 16"
        assert (
            ipu_inference_opts.replication_factor == 4
        ), "Expected replication_factor of ipu_inference_opts to be 4"
        assert ipu_training_opts._popart, "Expected _popart of ipu_training_opts to be True"
        assert ipu_inference_opts._popart, "Expected _popart of ipu_inference_opts to be True"
    except ImportError:
        pytest.skip("Skipping this test because poptorch is not available")