ValueError in `dask_glm.utils.zeros` with NumPy 2.x due to ambiguous truth value of empty array

[xlt208]

Describe the issue:
When running algorithms.admm() or algorithms.proximal_grad() from dask-glm with NumPy 2.x (e.g., 2.2.5), I encounter the following error:

ValueError: The truth value of an empty array is ambiguous. Use `array.size > 0` to check that an array is not empty.

Minimal Complete Verifiable Example:

from dask_glm import algorithms, datasets

X, y = datasets.make_regression(
    n_samples=200000, n_features=100, n_informative=5, chunksize=10000
)
b1 = algorithms.admm(X, y, max_iter=5)
b2 = algorithms.proximal_grad(X, y, max_iter=5)

Anything else we need to know?:
This appears to originate from the zeros() function in dask_glm/utils.py. In NumPy 2.x, evaluating if arr: is no longer valid when arr is an empty array. This breaks compatibility and causes the function to fail. See https://numpy.org/doc/stable/reference/arrays.ndarray.html.

dask-glm/dask_glm/utils.py

Lines 42 to 46 in 0c70593

	def zeros(shape, arr=None, **kwargs):
	if arr:
	return np.zeros_like(arr, shape=shape, **kwargs)
	else:
	return np.zeros(shape=shape, **kwargs)

The conditional in L43 should be updated to:

if arr is not None and arr.size > 0:

Environment:

• dask version: 2025.7.0
• dask-glm version: 0.3.2
• numpy version: 2.2.5
• Python version: 3.13
• Operating System: Windows/Linux
• Install method (conda, pip, source): conda