code: def _stable_inplace_diff_in_log(vec, signs, n=-1):
      """Replaces the first n-1 dims of vec with the log of abs difference operator.
  
      Args:
        vec: numpy array of floats with size larger than 'n'
        signs: Optional numpy array of bools with the same size as vec in case one
          needs to compute partial differences vec and signs jointly describe a
          vector of real numbers' sign and abs in log scale.
        n: Optonal upper bound on number of differences to compute. If negative, all
          differences are computed.
  
      Returns:
        The first n-1 dimension of vec and signs will store the log-abs and sign of
        the difference.
  
      Raises:
        ValueError: If input is malformed.
      """
  
      assert vec.shape == signs.shape
      if n < 0:
          n = np.max(vec.shape) - 1
      else:
          assert np.max(vec.shape) >= n + 1
      for j in range(0, n, 1):
          if signs[j] == signs[j + 1]:  # When the signs are the same
              # if the signs are both positive, then we can just use the standard one
              signs[j], vec[j] = _log_sub_sign(vec[j + 1], vec[j])
              # otherwise, we do that but toggle the sign
              if not signs[j + 1]:
                  signs[j] = ~signs[j]
          else:  # When the signs are different.
              vec[j] = _log_add(vec[j], vec[j + 1])
              signs[j] = signs[j + 1]
  
imports: import math
  from typing import Tuple, Union
  import numpy
  from scipy import special
  import six
label_desc: Use image node_id 11 for calling a global function with example usage: _stable_inplace_diff_in_log(vec, signs, n) without return types