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_activation_vars(self, input_act_vars):
      return tf.squeeze(
          tf.nn.max_pool(
              value=tf.expand_dims(input_act_vars, 1),
              ksize=(1, 1, self.pool_length, 1),
              strides=(1, 1, self.stride, 1),
              padding=self.padding,
          ),
          1,
      )
  
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 2 for calling the MaxPool1D obj's underlying member method code with example usage: obj._build_activation_vars(input_act_vars) and returns: tf