Fix numpy vector_norm(keepdims=True)

Author: asmeurer

array_api_compat/common/_linalg.py

@@ -110,21 +110,22 @@ def vector_norm(x: ndarray, /, xp, *, axis: Optional[Union[int, Tuple[int, ...]]
     # on a single dimension.
     if axis is None:
         # Note: xp.linalg.norm() doesn't handle 0-D arrays
-        x = x.ravel()
+        _x = x.ravel()
         _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.
         normalized_axis = normalize_axis_tuple(axis, x.ndim)
         rest = tuple(i for i in range(x.ndim) if i not in normalized_axis)
         newshape = axis + rest
-        x = xp.transpose(x, newshape).reshape(
+        _x = xp.transpose(x, newshape).reshape(
             (xp.prod([x.shape[i] for i in axis], dtype=int), *[x.shape[i] for i in rest]))
         _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