Fix meshgrid return type

CHANGELOG.md

@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 ### Changed
+* Changed `dpnp.meshgrid` and `dpnp.tensor.meshgrid` to return a tuple instead of a list, aligning with NumPy 2.5+ behavior and 2025.12 version of the Python array API standard [#2854](https://github.com/IntelPython/dpnp/pull/2854)
 
 ### Deprecated
 

dpnp/dpnp_iface_arraycreation.py

@@ -3111,7 +3111,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
         )
 
     if ndim < 1:
-        return []
+        return ()
 
     s0 = (1,) * ndim
     output = [
@@ -3132,7 +3132,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
     if copy:
         output = [dpt.copy(x) for x in output]
 
-    return [dpnp_array._create_from_usm_ndarray(x) for x in output]
+    return tuple(dpnp_array._create_from_usm_ndarray(x) for x in output)
 
 
 class MGridClass:

dpnp/tensor/_ctors.py

@@ -1441,7 +1441,7 @@ def linspace(
 
 def meshgrid(*arrays, indexing="xy"):
     """
-    Creates list of :class:`dpctl.tensor.usm_ndarray` coordinate matrices
+    Creates tuple of :class:`dpctl.tensor.usm_ndarray` coordinate matrices
     from vectors.
 
     Args:
@@ -1456,8 +1456,8 @@ def meshgrid(*arrays, indexing="xy"):
             keyword has no effect and should be ignored. Default: ``"xy"``
 
     Returns:
-        List[array]:
-            list of ``N`` arrays, where ``N`` is the number of
+        Tuple[array]:
+            tuple of ``N`` arrays, where ``N`` is the number of
             provided one-dimensional input arrays. Each returned array must
             have rank ``N``.
             For a set of ``n`` vectors with lengths ``N0``, ``N1``, ``N2``, ...
@@ -1495,7 +1495,7 @@ def meshgrid(*arrays, indexing="xy"):
         )
     n = len(arrays)
     if n == 0:
-        return []
+        return ()
 
     sh = (-1,) + (1,) * (n - 1)
 
@@ -1511,7 +1511,7 @@ def meshgrid(*arrays, indexing="xy"):
 
     output = dpt.broadcast_arrays(*res)
 
-    return output
+    return tuple(output)
 
 
 def ones(

dpnp/tests/tensor/test_usm_ndarray_ctor.py

@@ -1924,7 +1924,8 @@ def test_meshgrid():
     assert n == len(Znp)
     for i in range(n):
         assert np.array_equal(dpt.asnumpy(Z[i]), Znp[i])
-    assert dpt.meshgrid() == []
+    assert isinstance(Z, tuple)
+    assert dpt.meshgrid() == ()
     # dimension > 1 must raise ValueError
     with pytest.raises(ValueError):
         dpt.meshgrid(dpt.usm_ndarray((4, 4)))

dpnp/tests/test_arraycreation.py

@@ -984,13 +984,19 @@ def test_dpctl_tensor_input(func, args):
     [[], [[1]], [[1, 2, 3], [4, 5, 6]], [[1, 2], [3, 4], [5, 6]]],
     ids=["[]", "[[1]]", "[[1, 2, 3], [4, 5, 6]]", "[[1, 2], [3, 4], [5, 6]]"],
 )
-@pytest.mark.parametrize("dtype", get_all_dtypes(no_float16=False))
+@pytest.mark.parametrize(
+    "dtype", get_all_dtypes(no_none=True, no_float16=False)
+)
 @pytest.mark.parametrize("indexing", ["ij", "xy"])
 def test_meshgrid(arrays, dtype, indexing):
     func = lambda xp, xi: xp.meshgrid(*xi, indexing=indexing)
     a = tuple(numpy.array(array, dtype=dtype) for array in arrays)
     ia = tuple(dpnp.array(array, dtype=dtype) for array in arrays)
-    assert_array_equal(func(numpy, a), func(dpnp, ia))
+
+    result = func(dpnp, ia)
+    expected = func(numpy, a)
+    assert_array_equal(result, expected, strict=True)
+    assert isinstance(result, tuple)
 
 
 @pytest.mark.parametrize("shape", [(24,), (4, 6), (2, 3, 4), (2, 3, 2, 2)])

dpnp/tests/third_party/cupy/creation_tests/test_ranges.py

@@ -344,7 +344,7 @@ def test_meshgrid0(self, dtype):
         out = cupy.meshgrid(
             indexing=self.indexing, sparse=self.sparse, copy=self.copy
         )
-        assert out == []
+        assert out == ()
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_array_equal()