def METHOD_NAME(self):
    if not torch.cuda.is_available():
        return
    connection_types = [
        Connection,
        Conv2dConnection,
        MaxPool2dConnection,
        LocalConnection,
        MeanFieldConnection,
        SparseConnection,
    ]
    args = [[], [3], [3], [3, 1, 1], [], []]
    kwargs = [{}, {}, {}, {}, {}, {"sparsity": 0.9}]
    for conn_type, args, kwargs in zip(connection_types, args, kwargs):
        l_a = LIFNodes(shape=[1, 28, 28])
        l_b = LIFNodes(shape=[1, 26, 26])
        connection = conn_type(l_a, l_b, *args, **kwargs)
        connection.to()
        connection_tensors = [
            k
            for k, v in connection.state_dict().items()
            if isinstance(v, torch.Tensor) and not "." in k
        ]
        print(
            "State dict in {} : {}".format(
                conn_type, connection.state_dict().keys()
            )
        )
        print("__dict__ in {} : {}".format(conn_type, connection.__dict__.keys()))
        print("Tensors in {} : {}".format(conn_type, connection_tensors))
        tensor_devs = [getattr(connection, k).device for k in connection_tensors]
        print(
            "Tensor devices {}".format(list(zip(connection_tensors, tensor_devs)))
        )
        for d in tensor_devs:
            print(d, d == torch.device("cuda:0"))
            assert d == torch.device("cuda:0")