from numba import vectorize

@vectorize(['float64(float64, float64, UniTuple(float64, 9))'], target='cuda')
def fn_vec(E, L, fparams):
    # calculations...
    return result

TypeError: data type "(float64 x 9)" not understood

nb.types.containers.UniTuple(nb.types.float64, 9)

import numba as nb

@nb.njit(
    nb.types.float64(
        nb.types.float64,
        nb.types.float64,
        nb.types.containers.UniTuple(nb.types.float64, 9)))
def func(f1, f2, ftuple):
    # ...
    return f1

>>> nb.typeof((1.0, ) * 9)
tuple(float64 x 9)

>>> type(nb.typeof((1.0, ) * 9))
numba.types.containers.UniTuple

>>> help(type(nb.typeof((1.0, ) * 9)))  # I shortened the result:
Help on class UniTuple in module numba.types.containers:

class UniTuple(BaseAnonymousTuple, _HomogeneousTuple, numba.types.abstract.Sequence)
 |  UniTuple(*args, **kwargs)
 |
 |  Type class for homogeneous tuples.
 |
 |  Methods defined here:
 |
 |  __init__(self, dtype, count)
 |      Initialize self.  See help(type(self)) for accurate signature.