code: def _get_mxts_increments_for_inputs(self):
      if self.nonlinear_mxts_mode == NonlinearMxtsMode.DeconvNet:
          # apply the given nonlinearity in reverse
          pos_mxts = self._build_activation_vars(self.get_pos_mxts())
          neg_mxts = self._build_activation_vars(self.get_neg_mxts())
      else:
          # all the other ones here are of the form:
          # scale_factor*self.get_mxts()
          # recall that for all modes except RevealCancel, the treatment
          # of positive and negative terms is the same (and if RevealCancel
          # occurs nowhere, then pos_mxts=neg_mxts
          if self.nonlinear_mxts_mode == NonlinearMxtsMode.Rescale:
              scale_factor = self._get_naive_rescale_factor()
              pos_scale_factor = scale_factor
              neg_scale_factor = scale_factor
          elif (
              self.nonlinear_mxts_mode
              == NonlinearMxtsMode.GuidedBackpropRescale
          ):
              naive_scale_factor = self._get_naive_rescale_factor()
              pos_scale_factor = naive_scale_factor * hf.gt_mask(
                  self.get_pos_mxts(), 0.0
              )
              neg_scale_factor = naive_scale_factor * hf.gt_mask(
                  self.get_neg_mxts(), 0.0
              )
          elif self.nonlinear_mxts_mode == NonlinearMxtsMode.Gradient:
              scale_factor = self._gradients_get_scale_factor()
              pos_scale_factor = scale_factor
              neg_scale_factor = scale_factor
          elif (
              self.nonlinear_mxts_mode
              == NonlinearMxtsMode.GuidedBackprop
          ):
              scale_factor = (
                  self._gradients_get_scale_factor()
                  * hf.gt_mask(self.get_pos_mxts(), 0.0)
              )
              pos_scale_factor = scale_factor
              neg_scale_factor = scale_factor
          elif (
              self.nonlinear_mxts_mode == NonlinearMxtsMode.RevealCancel
          ):
              (
                  pos_contribs,
                  neg_contribs,
              ) = self.get_pos_and_neg_contribs()
              (
                  input_pos_contribs,
                  input_neg_contribs,
              ) = self._get_input_pos_and_neg_contribs()
              pos_scale_factor = (
                  pos_contribs
                  / hf.pseudocount_near_zero(input_pos_contribs)
              )
              neg_scale_factor = (
                  neg_contribs
                  / hf.pseudocount_near_zero(input_neg_contribs)
              )
          elif (
              self.nonlinear_mxts_mode == NonlinearMxtsMode.PassThrough
          ):
              pos_scale_factor = 1.0
              neg_scale_factor = 1.0
          else:
              raise RuntimeError(
                  "Unsupported nonlinear_mxts_mode: "
                  + str(self.nonlinear_mxts_mode)
              )
          pos_mxts = pos_scale_factor * self.get_pos_mxts()
          neg_mxts = neg_scale_factor * self.get_neg_mxts()
      return pos_mxts, neg_mxts
  
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 10 for calling the Activation obj's underlying member method code with example usage: obj._get_mxts_increments_for_inputs() and returns: pos_mxts,  neg_mxts