revert

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))