Hello!
I am trying to understand a basic example of signatures for @vectorize targetting cuda and a some doubts appeared.
From here Types and signatures — Numba 0.52.0.dev0+274.g626b40epy3.7linuxx86_64.egg documentation I understand that:

"float64(int32, int32)"
) which specifies a function taking two 32bit integers and returning a doubleprecision float.  Given the former information, I would expect the following signature in the code below (CUDA Ufuncs and Generalized Ufuncs — Numba 0.50.1 documentation)
@vectorize(['float64(float32, float32, float32)']
(Output a float64 and input 3 arrays of float32)
So my questions are:
 Why isn´t it
@vectorize(['float64(float32, float32, float32)']
?  To understand how to pass my signatures, how could I for instance use the nb.typeof to create my signatures? For instance if I had TWO input array having the type array(float32, 2d, C) that outputs me TWO single float64 (not an array) would the signature in this case be
@vectorize(['float64,float64(float32, float32)']
?
import math
from numba import vectorize, cuda
import numpy as np
@vectorize(['float32(float32, float32, float32)',
'float64(float64, float64, float64)'],
target='cuda')
def cu_discriminant(a, b, c):
return math.sqrt(b ** 2  4 * a * c)
N = 10000
dtype = np.float32
# prepare the input
A = np.array(np.random.sample(N), dtype=dtype)
B = np.array(np.random.sample(N) + 10, dtype=dtype)
C = np.array(np.random.sample(N), dtype=dtype)
D = cu_discriminant(A, B, C)
print(D) # print result
Many thanks!