unnable to wrap function with da.as_gufunc for cupy array

**Describe the issue**:
I was happy to use `dask-image` with processing many images with CPU. Now I want to look whether this task can be rather accelerated with GPU. Previously I defined my custom function in this way:
```
@da.as_gufunc(signature=f"(i,j),(),()->(i,j)", output_dtypes=np.int16, vectorize=True)
def cpu_convolve_peak(img, th, dis):
mask_hor_np = np.ones([1,2])
clusters = np.fftconvolve(img, mask_hor_np, mode='same')
# doing some further stuff with clusters as local peak search etc
return clusters
```
and applied it to images loaded with `dask_image.imread.imread("...", arraytype="numpy" )` and it was perfekt.

Now I loading my images with `cp_images = dask_image.imread.imread("...", arraytype="cupy" )` and have a python function which doing some sort of complex things inside using GPU-only functions inside.
```
@da.as_gufunc(signature=f"(i,j),(),()->(i,j)", output_dtypes=cp.int16, vectorize=True)
def gpu_convolve_peak(img, th, dis):
mask_hor_cp = cp.ones([1,2])
clusters = cuscipy.fftconvolve(img, mask_hor_cp, mode='same')
# doing some further stuff with clusters as local peak search etc
return clusters
```
When now I try to execute the following code:
```
gpu_convolve_peak(cp_images, 100, 2).compute()
```
or either apply more functions at the end, for example:
```
gpu_convolve_peak(cp_images, 100, 2).sum(axis=0).compute()
```
I will get an error:
```TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.```

It seems like dask inside tries to handle the array as numpy array despite it is cupy array. I would also ask whether my approach is correct in a sense of CUDA architecture. When I executing a cupy function it will work only in a "single core mode" on the GPU, right? I mean that GPU doesn't parallelize it itself and I need to run more instances to get this kind of "GPU multicore supremacy" with all these 2000+ CUDA cores on my GPU. I would be very thankful if you correct me and tell what the right way would be.

Thank you so much for your beautiful project and nice code!

**Environment**:

- Dask-image version: 2022.9.0
- Cupy version: 11.3
- Python version: 3.8.15
- Operating System: Ubuntu 20.04.4 LTS x86_64
- Install method (conda, pip, source): conda
- CUDA version: 11.5
- GPU: Nvidia GTX 1070Ti