def METHOD_NAME(self):
    """
    Test inference accuracy (for lazy loading based trainings).
    What is explicitly tested is that the scaling and rescaling do not
    change the values too drastically. During inference (or at least
    in the test case) this is done.
    """
    # Without lazy loading, there is a bigger numerical error in the actual
    # outputs. This is due to the fact that we scale and rescale data on 32
    # byte float arrays; lazy loading is therefore drastically advised for
    # inference/testing purposes.
    batchsizes = [46, 99, 500, 1977]
    for batchsize in batchsizes:
        actual_ldos, from_file, predicted_ldos, raw_predicted_outputs = \
            self.__run(use_lazy_loading=True, batchsize=batchsize)
        assert np.isclose(actual_ldos.sum(), from_file.sum(),
                          atol=accuracy_strict)