Update broadcast_arrays to return a tuple instead of a list

CHANGELOG.md

@@ -37,6 +37,7 @@ This release is compatible with NumPy 2.5.
 * Aligned the signature of `dpnp.tensor.expand_dims` with the Python array API by making `axis` a required argument [#2988](https://github.com/IntelPython/dpnp/pull/2988)
 * Removed dead code branches guarded by outdated oneMKL and DPC++ compiler version checks [#2999](https://github.com/IntelPython/dpnp/pull/2999)
 * Replaced the deprecated `nd_item::barrier()` member calls in the `accumulators` and `gemm` kernels with the SYCL 2020 `sycl::group_barrier()` free function [#3006](https://github.com/IntelPython/dpnp/pull/3006)
+* Changed `dpnp.broadcast_arrays` and `dpnp.tensor.broadcast_arrays` to return a tuple instead of a list, aligning with the 2025.12 Python array API spec [#2944](https://github.com/IntelPython/dpnp/pull/2944)
 
 ### Deprecated
 

dpnp/dpnp_iface_manipulation.py

@@ -1060,8 +1060,8 @@ def broadcast_arrays(*args, subok=False):
 
     Returns
     -------
-    out : list of dpnp.ndarray
-        A list of arrays which are views on the original arrays from `args`.
+    out : tuple of dpnp.ndarray
+        A tuple of arrays which are views on the original arrays from `args`.
 
     Limitations
     -----------
@@ -1078,20 +1078,20 @@ def broadcast_arrays(*args, subok=False):
     >>> x = np.array([[1, 2, 3]])
     >>> y = np.array([[4], [5]])
     >>> np.broadcast_arrays(x, y)
-    [array([[1, 2, 3],
+    (array([[1, 2, 3],
             [1, 2, 3]]), array([[4, 4, 4],
-            [5, 5, 5]])]
+            [5, 5, 5]]))
 
     """
 
     if subok is not False:
         raise NotImplementedError(f"subok={subok} is currently not supported")
 
     if len(args) == 0:
-        return []
+        return ()
 
     usm_arrays = dpt.broadcast_arrays(*[dpnp.get_usm_ndarray(a) for a in args])
-    return [dpnp_array._create_from_usm_ndarray(a) for a in usm_arrays]
+    return tuple(dpnp_array._create_from_usm_ndarray(a) for a in usm_arrays)
 
 
 def broadcast_shapes(*args):

dpnp/tensor/_manipulation_functions.py

@@ -228,8 +228,8 @@ def broadcast_arrays(*args):
             broadcasted.
 
     Returns:
-        List[usm_ndarray]:
-            A list of broadcasted arrays. Each array
+        tuple[usm_ndarray, ...]:
+            A tuple of broadcasted arrays. Each array
             must have the same shape. Each array must have the same `dtype`,
             `device` and `usm_type` attributes as its corresponding input
             array.
@@ -245,7 +245,7 @@ def broadcast_arrays(*args):
     if all(X.shape == shape for X in args):
         return args
 
-    return [broadcast_to(X, shape) for X in args]
+    return tuple(broadcast_to(X, shape) for X in args)
 
 
 def broadcast_to(X, /, shape):

dpnp/tests/tensor/test_usm_ndarray_manipulation.py

@@ -464,6 +464,14 @@ def test_incompatible_shapes_raise_valueerror(shapes):
         assert_broadcast_arrays_raise(input_shapes[::-1])
 
 
+def test_broadcast_arrays_tuple():
+    q = get_queue_or_skip()
+    out = dpt.broadcast_arrays(
+        dpt.ones((1, 3), sycl_queue=q), dpt.zeros((3, 1), sycl_queue=q)
+    )
+    assert isinstance(out, tuple)
+
+
 def test_broadcast_arrays_no_args():
     with pytest.raises(ValueError):
         dpt.broadcast_arrays()

dpnp/tests/test_arraymanipulation.py

@@ -294,8 +294,12 @@ def test_incompatible_shapes_raise_valueerror(self, shapes):
             self.assert_broadcast_arrays_raise(input_shapes)
             self.assert_broadcast_arrays_raise(input_shapes[::-1])
 
+    def test_broadcast_arrays_tuple(self):
+        out = dpnp.broadcast_arrays(dpnp.ones((1, 3)), dpnp.zeros((3, 1)))
+        assert isinstance(out, tuple)
+
     def test_broadcast_arrays_empty_input(self):
-        assert dpnp.broadcast_arrays() == []
+        assert dpnp.broadcast_arrays() == ()
 
     def test_subok_error(self):
         x = dpnp.ones(4)

dpnp/tests/third_party/cupy/manipulation_tests/test_dims.py

@@ -315,6 +315,10 @@ def test_broadcast_arrays(self, xp, dtype):
         arrays = [testing.shaped_arange(s, xp, dtype) for s in self.shapes]
         return xp.broadcast_arrays(*arrays)
 
+    def test_broadcast_arrays_tuple(self):
+        out = cupy.broadcast_arrays(cupy.ones(3), cupy.zeros(3))
+        assert isinstance(out, tuple)
+
 
 @testing.parameterize(
     {"shapes": [(3,), (2,)]},