code: def _range_normalizer(x, margin):
      x = x.flatten()
      min_x = np.min(x)
      max_x = np.max(x)
      a = margin * (2.0 / (max_x - min_x))
      b = margin * (-2.0 * min_x / (max_x - min_x) - 1.0)
      return a, b
  
imports: import io
  import os
  from absl import logging
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: _range_normalizer(x, margin) and returns: a,  b