Fix JAX sparse array flat indexing#2091
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Summary
array_from_sparsein the JAX backend by applying raveled indices to a flattened dense array before reshaping back to the requested target shape.Why
The previous implementation computed flat/raveled indices with
jnp.ravel_multi_index, but then used those positions to index the original n-D output array. For multidimensional target shapes this indexes the first axis rather than the flattened storage order, which can write values to the wrong positions or raise out-of-bounds errors.Tests
tests/backend/test_jax_array_from_sparse.pyfor CI coverage.