TST: un-xfail tests of take(..., out=out)

Author: ev-br

torch_np/_funcs.py

@@ -929,10 +929,15 @@ def asfarray():
 
 # ### put/take_along_axis ###
 
+
 @normalizer
-def take(a: ArrayLike, indices: ArrayLike, axis=None, out : Optional[NDArray]=None, mode="raise"):
-    if out is not None:
-        raise NotImplementedError(f"{out=}")
+def take(
+    a: ArrayLike,
+    indices: ArrayLike,
+    axis=None,
+    out: Optional[NDArray] = None,
+    mode="raise",
+):
     if mode != "raise":
         raise NotImplementedError(f"{mode=}")
 
@@ -943,8 +948,6 @@ def take(a: ArrayLike, indices: ArrayLike, axis=None, out : Optional[NDArray]=No
     return result
 
 
-
-
 @normalizer
 def take_along_axis(arr: ArrayLike, indices: ArrayLike, axis):
     (arr,), axis = _util.axis_none_ravel(arr, axis=axis)

torch_np/tests/numpy_tests/core/test_multiarray.py

@@ -3851,14 +3851,13 @@ def test_wrap(self):
         assert_array_equal(np.take(x, [2], axis=0, mode='wrap')[0], x[0])
         assert_array_equal(np.take(x, [3], axis=0, mode='wrap')[0], x[1])
 
-    @pytest.mark.xfail(reason="XXX: take(out=...)")
+    @pytest.mark.xfail(reason="XXX: take(mode='wrap')")
     def test_out_overlap(self):
         # gh-6272 check overlap on out
         x = np.arange(5)
         y = np.take(x, [1, 2, 3], out=x[2:5], mode='wrap')
         assert_equal(y, np.array([1, 2, 3]))
 
-    @pytest.mark.xfail(reason="XXX: take(out=...)")
     @pytest.mark.parametrize('shape', [(1, 2), (1,), ()])
     def test_ret_is_out(self, shape):
         # 0d arrays should not be an exception to this rule