From 92979229874996b6aaf6dec1edd75087ff94e1a2 Mon Sep 17 00:00:00 2001
From: Ushnah Abbasi <ushnah.abbasi@arbisoft.com>
Date: Tue, 4 Aug 2026 17:10:37 +0500
Subject: [PATCH] test: adjust nansum regression coverage

---
 tests/test_funcs.py | 126 ++++++++++++++++++++++----------------------
 1 file changed, 63 insertions(+), 63 deletions(-)

diff --git a/tests/test_funcs.py b/tests/test_funcs.py
index b2de6a44..cf2a3d26 100644
--- a/tests/test_funcs.py
+++ b/tests/test_funcs.py
@@ -2315,69 +2315,6 @@ def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespa
         assert_equal(res, expected)
 
 
-class TestNanSum:
-    def test_simple(self, xp: ArrayNamespace):
-        a = xp.asarray([[1.0, 2.0], [3.0, xp.nan]])
-
-        res = nansum(a)
-        expected = 6.0
-        assert res == expected
-
-        res = nansum(a, axis=0)
-        expected = xp.asarray([4.0, 2.0])
-        assert_equal(res, expected)
-
-        res = nansum(a, axis=1)
-        expected = xp.asarray([3.0, 3.0])
-        assert_equal(res, expected)
-
-    def test_bigger(self, xp: ArrayNamespace):
-        a = xp.asarray(
-            [
-                [1.0, xp.nan, 4.0, 5.0],
-                [xp.nan, -2.0, xp.nan, -4.0],
-                [2.0, 1.0, 3.0, xp.nan],
-            ]
-        )
-
-        res = nansum(a, axis=0)
-        expected = xp.asarray([3.0, -1.0, 7.0, 1.0])
-        assert_equal(res, expected)
-
-        res = nansum(a, axis=1)
-        expected = xp.asarray([10.0, -6.0, 6.0])
-        assert_equal(res, expected)
-
-    def test_all_nan_slice(self, xp: ArrayNamespace):
-        a = xp.asarray([[xp.nan, 1.0], [xp.nan, xp.nan]])
-
-        res = nansum(a, axis=0)
-        expected = xp.asarray([0.0, 1.0])
-        assert_equal(res, expected)
-
-    def test_scalar(self, xp: ArrayNamespace):
-        a = xp.asarray(1.0)
-        assert nansum(a) == 1.0
-
-    @pytest.mark.skip_xp_backend(
-        Backend.TORCH, reason="torch.nansum does not support tensors on meta device"
-    )
-    @pytest.mark.parametrize("axis", [None, 0, 1])
-    def test_device(self, axis: int | None, xp: ArrayNamespace, device: Device):
-        a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 5.0, xp.nan]], device=device)
-        res = nansum(a, axis=axis)
-        assert get_device(res) == device
-
-    @pytest.mark.parametrize(
-        ("axis", "expected_list"), [(0, [6.0, 3.0, 1.0]), (1, [5.0, 5.0])]
-    )
-    def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespace):
-        a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 3.0, xp.nan]])
-        res = nansum(a, axis=axis, xp=xp)
-        expected = xp.asarray(expected_list)
-        assert_equal(res, expected)
-
-
 class TestNanMax:
     def test_simple(self, xp: ArrayNamespace):
         a = xp.asarray([[5, 3], [6, xp.nan]])
@@ -2457,3 +2394,66 @@ def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespa
         res = nanmax(a, axis=axis, xp=xp)
         expected = xp.asarray(expected_list)
         assert_equal(res, expected)
+
+
+class TestNanSum:
+    def test_simple(self, xp: ArrayNamespace):
+        a = xp.asarray([[1.0, 2.0], [3.0, xp.nan]])
+
+        res = nansum(a)
+        expected = 6.0
+        assert res == expected
+
+        res = nansum(a, axis=0)
+        expected = xp.asarray([4.0, 2.0])
+        assert_equal(res, expected)
+
+        res = nansum(a, axis=1)
+        expected = xp.asarray([3.0, 3.0])
+        assert_equal(res, expected)
+
+    def test_bigger(self, xp: ArrayNamespace):
+        a = xp.asarray(
+            [
+                [1.0, xp.nan, 4.0, 5.0],
+                [xp.nan, -2.0, xp.nan, -4.0],
+                [2.0, 1.0, 3.0, xp.nan],
+            ]
+        )
+
+        res = nansum(a, axis=0)
+        expected = xp.asarray([3.0, -1.0, 7.0, 1.0])
+        assert_equal(res, expected)
+
+        res = nansum(a, axis=1)
+        expected = xp.asarray([10.0, -6.0, 6.0])
+        assert_equal(res, expected)
+
+    def test_all_nan_slice(self, xp: ArrayNamespace):
+        a = xp.asarray([[xp.nan, 1.0], [xp.nan, xp.nan]])
+
+        res = nansum(a, axis=0)
+        expected = xp.asarray([0.0, 1.0])
+        assert_equal(res, expected)
+
+    def test_scalar(self, xp: ArrayNamespace):
+        a = xp.asarray(1.0)
+        assert nansum(a) == 1.0
+
+    @pytest.mark.skip_xp_backend(
+        Backend.TORCH, reason="torch.nansum does not support tensors on meta device"
+    )
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    def test_device(self, axis: int | None, xp: ArrayNamespace, device: Device):
+        a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 5.0, xp.nan]], device=device)
+        res = nansum(a, axis=axis)
+        assert get_device(res) == device
+
+    @pytest.mark.parametrize(
+        ("axis", "expected_list"), [(0, [6.0, 3.0, 1.0]), (1, [5.0, 5.0])]
+    )
+    def test_xp(self, axis: int | None, expected_list: list[float], xp: ArrayNamespace):
+        a = xp.asarray([[4.0, xp.nan, 1.0], [2.0, 3.0, xp.nan]])
+        res = nansum(a, axis=axis, xp=xp)
+        expected = xp.asarray(expected_list)
+        assert_equal(res, expected)