def __init__(self, N, verbose=False, measure_args={}, **kwargs):
        """ Slow (n^3, or n^2 if step sizes are cached) version via manual matrix mult/inv

        delta: optional list of step sizes to cache the transitions for
        """
        A, B = transition(type(self).measure, N, **measure_args)
        A = torch.as_tensor(A, dtype=torch.float)
        B = torch.as_tensor(B, dtype=torch.float)[:, 0]
        super().__init__(N, A, B, **kwargs)


        if verbose:
            print(f"{self.__class__}\n  A {self.A}\nB {self.B}")
 Slow (n^3, or n^2 if step sizes are cached) version via manual matrix mult/inv

        delta: optional list of step sizes to cache the transitions for
        
analyze/convnexts4nd/src/models/hippo/transition.py