Typing necessary for static Python-Numpy compilers like Pythran and Cython

首先检查当前 array-api-typing 的能力，并将其与此 issue 中的 Transonic 示例进行比较，包括融合数据类型和数组维度。完成标准是确定这些签名目前是否可以表达；如果不能，则定义缺失的能力及其未来可能的发展方向。

Static Python-Numpy compilers like Pythran and Cython (and others) need type information.

For Transonic (a tools to use Python-Numpy compilers, see https://transonic.readthedocs.io), I had to implement a simple system to be able to give enough information in Python about the types supported by functions.

I give here an example of what is possible (more complex things are sometimes needed but this is the minimum, fused data-types and "fused" number of dimensions of the array):

import numpy as np

from transonic import Array, NDim, Type, boost

T = Type(int, np.complex128)
N = NDim(1, 3)

A = Array[T, N]
A1 = Array[np.float32, N + 1]

@boost
def compute(a: A, b: A, c: T, d: A1, e: str):
    ...

It would be great if array-api-typing could be good enough to be able to express such things in a quite simple way.

Note that this leads to the following possible Pythran signatures:

export compute(complex128[:, :, :], complex128[:, :, :], complex128, float32[:, :, :, :], str)
export compute(complex128[:], complex128[:], complex128, float32[:, :], str)
export compute(int[:, :, :], int[:, :, :], int, float32[:, :, :, :], str)
export compute(int[:], int[:], int, float32[:, :], str)

and Cython "signatures":

import cython

import numpy as np
cimport numpy as np

ctypedef fused __compute__Array_TypeIint_complex128I_NDimI1_3I:
   np.ndarray[np.complex128_t, ndim=1]
   np.ndarray[np.complex128_t, ndim=3]
   np.ndarray[np.int32_t, ndim=1]
   np.ndarray[np.int32_t, ndim=3]

ctypedef fused __compute__Array_float32_NDimI1_3Ip1:
   np.ndarray[np.float32_t, ndim=2]
   np.ndarray[np.float32_t, ndim=4]

ctypedef fused __compute__TypeIint_complex128I:
   cython.int
   np.complex128_t

cpdef compute(__compute__Array_TypeIint_complex128I_NDimI1_3I a, __compute__Array_TypeIint_complex128I_NDimI1_3I b, __compute__TypeIint_complex128I c, __compute__Array_float32_NDimI1_3Ip1 d, cython.str e)

Is there already enough in array-api-typing to be able to express such things? If not, would it be possible in future?