aboderinsamuel opened a new pull request, #50203:
URL: https://github.com/apache/arrow/pull/50203
### Rationale for this change
Constructing a fixed-shape-tensor array from a list of individual ndarrays
only
worked when each element was 1-D; ≥2-D elements failed with
`ArrowInvalid: Can only convert 1-dimensional array values`. The only
workaround
was stacking the list into a single ndarray and using
`FixedShapeTensorArray.from_numpy_ndarray`.
### What changes are included in this PR?
The C++ list converter `PyListConverter::AppendNdarray` now accepts
multi-dimensional ndarray elements for **fixed-size lists** (the storage of a
fixed-shape tensor) by flattening them in C order. The fixed-size-list
builder
still validates that the flattened length matches the list width, so wrong
sizes
error cleanly. Variable-sized lists remain restricted to 1-D values to avoid
ambiguity. As a side benefit, plain `fixed_size_list` also accepts
multi-dimensional ndarray elements now.
### Are these changes tested?
Yes:
- `test_tensor_array_from_list_of_ndarrays` — construction from 2-D and 3-D
ndarrays, null handling, storage parity with `from_numpy_ndarray`, and the
size-mismatch error, across `int8`/`int64`/`float32`.
- `test_fixed_size_list_from_multidim_ndarray` — plain `fixed_size_list` from
multi-dim arrays, plus a check that variable-sized lists still reject 2-D.
### Are there any user-facing changes?
Yes — `pa.array([multi-dim ndarrays], type=fixed_shape_tensor(...))` (and the
same for `fixed_size_list`) now works instead of raising. Existing 1-D
behavior
and variable-sized-list behavior are unchanged.
Scoped to construction only; the reverse `to_numpy` shape-preservation also
raised in the issue is intentionally left as a separate follow-up.
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