code: def scale_neg_1_to_1_with_zero_mean_rank_abs_max(v):
      rankv = v * 2 - 1
      pos_v = rankv[rankv > 0]
      pos_v = rankdata(pos_v, "dense")
      pos_v = pos_v / pos_v.max()
      neg_v = rankv[rankv < 0]
      neg_v = rankdata(neg_v, "dense")
      neg_v = neg_v / neg_v.max()
      rankv[rankv > 0] = pos_v
      rankv[rankv < 0] = -(neg_v.max() - neg_v)
  
      return scale_neg_1_to_1_with_zero_mean_abs_max(rankv)
  
imports: from typing import Callable
  import numpy
  import pandas
  from scipy.stats import rankdata
label_desc: Use image node_id 10 for calling a global function with example usage: scale_neg_1_to_1_with_zero_mean_rank_abs_max(v) and returns: scale_neg_1_to_1_with_zero_mean_abs_max