message: clean the pollution of axis
diff: @@ -543,13 +543,12 @@ def norm(tensor,
  if ord in ['fro', 'euclidean', 2, 2.0]:
  if is_matrix_norm and ord in [2, 2.0]:
  rank = array_ops.rank(tensor)
  - axis = functional_ops.map_fn(
  - lambda i: control_flow_ops.cond(i >= 0, lambda: i,
  - lambda: i + rank),
  + positive_axis = functional_ops.map_fn(
  + lambda i: control_flow_ops.cond(i >= 0, lambda: i, lambda: i + rank),
  ops.convert_to_tensor(axis))
  axes = math_ops.range(rank)
  perm_before = array_ops.concat(
  - [array_ops.setdiff1d(axes, axis)[0], axis], axis=0)
  + [array_ops.setdiff1d(axes, positive_axis)[0], positive_axis], axis=0)
  perm_after = functional_ops.map_fn(
  lambda i: math_ops.cast(
  array_ops.squeeze(
  @@ -557,8 +556,11 @@ def norm(tensor,
  dtype=dtypes.int32), axes)
  permed = array_ops.transpose(tensor, perm=perm_before)
  matrix_2_norm = array_ops.expand_dims(
  - math_ops.reduce_max(gen_linalg_ops.svd(permed, compute_uv=False)[0],
  - axis=-1, keepdims=True), axis=-1)
  + math_ops.reduce_max(
  + gen_linalg_ops.svd(permed, compute_uv=False)[0],
  + axis=-1,
  + keepdims=True),
  + axis=-1)
  result = array_ops.transpose(matrix_2_norm, perm=perm_after)
  else:
  result = math_ops.sqrt(
  