code: def _get_naive_rescale_factor(self):
      input_diff_from_reference = (
          self._get_input_diff_from_reference_vars()
      )
      near_zero_contrib_mask = hf.lt_mask(
          tf.abs(input_diff_from_reference), NEAR_ZERO_THRESHOLD
      )
      far_from_zero_contrib_mask = 1.0 - near_zero_contrib_mask
      # the pseudocount is to avoid division-by-zero for the ones that
      # we won't use anyway
      pc_diff_from_reference = input_diff_from_reference + (
          1.0 * near_zero_contrib_mask
      )
      # when total contrib is near zero,
      # the scale factor is 1 (gradient; piecewise linear). Otherwise,
      # compute the scale factor. The pseudocount doesn't mess anything up
      # as it is only there to prevent division by zero for the cases where
      # the contrib is near zero.
      scale_factor = (
          near_zero_contrib_mask
          * self._get_gradient_at_default_activation_var()
          + (
              far_from_zero_contrib_mask
              * (
                  self._get_diff_from_reference_vars()
                  / pc_diff_from_reference
              )
          )
      )
      return scale_factor
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  from .core import SingleInputMixin, OneDimOutputMixin, Node, ScoringMode, NonlinearMxtsMode
  import tensorflow
  from deeplift.util import NEAR_ZERO_THRESHOLD
  from deeplift.layers import helper_functions
label_desc: Use image node_id 8 for calling the Activation obj's underlying member method code with example usage: obj._get_naive_rescale_factor() and returns: scale_factor