Usage: Finch backend returns Julia-native dtypes like 'Julia: Float64.' Is this intentional?

Please provide a description of what you'd like to do.

Hello,

I’ve run into an issue when using the Finch backend via pydata/sparse, specifically with libraries that expect NumPy-compatible dtypes (e.g. when calling np.dtype(...) on sparse arrays).

When I set os.environ["SPARSE_BACKEND"] = "Finch"

and create a COO array from a NumPy array:

import numpy as np

os.environ["SPARSE_BACKEND"] = "Finch"
import sparse

sa = sparse.COO.from_numpy(np.eye(4))
print(sa.dtype)  # returns <class 'finch.tensor.Tensor'>

This causes downstream failures in libraries that assume they can call np.dtype(sa.dtype) with the following error TypeError: Cannot interpret 'Julia: Float64' as a data type.

So I have the following questions:

• Is returning 'Julia: Float64' as a dtype string intentional behavior?
• Are there plans to return NumPy-compatible dtype objects instead?
• Are there known workarounds for this kind of downstream compatibility issue?

I wasn’t able to find a discussion of this in the issue tracker, apologies if I missed it.

Thanks so much!

/cc @ilan-gold

Example Code

No response

Reproduce the reported path by setting SPARSE_BACKEND=Finch and calling sparse.COO.from_numpy(np.eye(4)), then inspect the resulting dtype and its behavior with np.dtype. Trace the backend dtype handling and determine whether the compatibility behavior is intentional; done means the expected dtype behavior or workaround is established and covered by an appropriate regression test.