revert

Author: crusaderky

array_api_compat/common/_linalg.py

@@ -164,7 +164,7 @@ def vector_norm(
     if axis is None:
         # Note: xp.linalg.norm() doesn't handle 0-D arrays
         _x = x.ravel()
-        axis = 0
+        _axis = 0
     elif isinstance(axis, tuple):
         # Note: The axis argument supports any number of axes, whereas
         # xp.linalg.norm() only supports a single axis for vector norm.
@@ -176,24 +176,25 @@ def vector_norm(
         newshape = axis + rest
         _x = xp.transpose(x, newshape).reshape(
             (math.prod([x.shape[i] for i in axis]), *[x.shape[i] for i in rest]))
-        axis = 0
+        _axis = 0
     else:
         _x = x
+        _axis = axis
 
-    res = xp.linalg.norm(_x, axis=axis, ord=ord)
+    res = xp.linalg.norm(_x, axis=_axis, ord=ord)
 
     if keepdims:
         # We can't reuse xp.linalg.norm(keepdims) because of the reshape hacks
         # above to avoid matrix norm logic.
         shape = list(x.shape)
-        axis = cast(
+        _axis = cast(
             "tuple[int, ...]",
             normalize_axis_tuple(  # pyright: ignore[reportCallIssue]
                 range(x.ndim) if axis is None else axis,
                 x.ndim,
             ),
         )
-        for i in axis:
+        for i in _axis:
             shape[i] = 1
         res = xp.reshape(res, tuple(shape))