class_docstr: Heads-up: an all-or-none MaxPoolDeepLiftMode is only 
      appropriate when all inputs falling within a single
      kernel have the same default value.
  Heads-up: scaled all-or-none MaxPoolDeepLiftMode can
      lead to odd results if the inputs falling within a
      single kernel don't have approx even default vals
class_docstr_tok: ["Heads-up",":","an","all-or-none","MaxPoolDeepLiftMode","is","only","appropriate","when","all","inputs","falling","within","a","single","kernel","have","the","same","default","value",".","Heads-up",":","scaled","all-or-none","MaxPoolDeepLiftMode","can","lead","to","odd","results","if","the","inputs","falling","within","a","single","kernel","do","n't","have","approx","even","default","vals"]
code: def _get_mxts_increments_for_inputs(self):
      if self.maxpool_deeplift_mode == MaxPoolDeepLiftMode.gradient:
          pos_mxts_increments = self._grad_op(self.get_pos_mxts())
          neg_mxts_increments = self._grad_op(self.get_neg_mxts())
      else:
          raise RuntimeError(
              "Unsupported maxpool_deeplift_mode: "
              + str(self.maxpool_deeplift_mode)
          )
      return pos_mxts_increments, neg_mxts_increments
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  from .core import *
  from .helper_functions import conv1d_transpose_via_conv2d
  from .convolutional import PaddingMode, DataFormat
  from .None import helper_functions
  import tensorflow
  from tensorflow.python.ops import nn_grad
label_desc: Use image node_id 5 for calling the MaxPool1D obj's underlying member method code with example usage: obj._get_mxts_increments_for_inputs() and returns: pos_mxts_increments,  neg_mxts_increments