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 _build_pos_and_neg_contribs(self):
      if self.verbose:
          print(
              "Heads-up: current implementation assumes maxpool layer "
              "is followed by a linear transformation (conv/dense layer)"
          )
      # placeholder; not used for linear layer, hence assumption above
      return tf.zeros_like(
          tensor=self.get_activation_vars(),
          name="dummy_pos_cont_" + str(self.get_name()),
      ), tf.zeros_like(
          tensor=self.get_activation_vars(),
          name="dummy_neg_cont_" + str(self.get_name()),
      )
  
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 3 for calling the MaxPool2D obj's underlying member method code with example usage: obj._build_pos_and_neg_contribs() and returns: tf,  tf