How to use the torchdiffeq._impl.rk_common._ButcherTableau function in torchdiffeq

# Based on https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/integrate
import torch
from .misc import (
    _scaled_dot_product, _convert_to_tensor, _is_finite, _select_initial_step, _handle_unused_kwargs, _is_iterable,
    _optimal_step_size, _compute_error_ratio
)
from .solvers import AdaptiveStepsizeODESolver
from .interp import _interp_fit, _interp_evaluate
from .rk_common import _RungeKuttaState, _ButcherTableau, _runge_kutta_step

_DORMAND_PRINCE_SHAMPINE_TABLEAU = _ButcherTableau(
    alpha=[1 / 5, 3 / 10, 4 / 5, 8 / 9, 1., 1.],
    beta=[
        [1 / 5],
        [3 / 40, 9 / 40],
        [44 / 45, -56 / 15, 32 / 9],
        [19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729],
        [9017 / 3168, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656],
        [35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84],
    ],
    c_sol=[35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84, 0],
    c_error=[
        35 / 384 - 1951 / 21600,
        0,
        500 / 1113 - 22642 / 50085,
        125 / 192 - 451 / 720,
        -2187 / 6784 - -12231 / 42400,

# Based on https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/integrate
import torch
from .misc import (
    _scaled_dot_product, _convert_to_tensor, _is_finite, _select_initial_step, _handle_unused_kwargs, _is_iterable,
    _optimal_step_size, _compute_error_ratio
)
from .solvers import AdaptiveStepsizeODESolver
from .interp import _interp_fit, _interp_evaluate
from .rk_common import _RungeKuttaState, _ButcherTableau, _runge_kutta_step

_ADAPTIVE_HEUN_TABLEAU = _ButcherTableau(
    alpha=[1.],
    beta=[
        [1.],
    ],
    c_sol=[0.5, 0.5],
    c_error=[
        0.5,
        -0.5,
    ],
)

AH_C_MID = [
    0.5, 0.
]