old_contents: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  
  import numpy as np
  import tensorflow as tf
  
  def get_sequence_length(sequence, scope=None):
      """
      This is a hacky way of determining the actual length of a sequence that has been padded with zeros.
      """
      with tf.variable_scope(scope, 'SequenceLength'):
          used = tf.sign(tf.reduce_max(tf.abs(sequence), reduction_indices=[-1]))
          length = tf.cast(tf.reduce_sum(used, reduction_indices=[-1]), tf.int32)
          return length
  
  def get_sequence_mask(sequence, scope=None):
      """
      This is a hacky way of masking the padded sentence embeddings.
      """
      with tf.variable_scope(scope, 'SequenceMask'):
          sequence = tf.reduce_sum(sequence, reduction_indices=[-1], keep_dims=True)
          mask = tf.to_float(tf.greater(sequence, 0))
          mask = tf.expand_dims(mask, -1)
          return mask
  
new_contents: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  
  import numpy as np
  import tensorflow as tf
  
  def get_sequence_length(sequence, scope=None):
      """
      This is a hacky way of determining the actual length of a sequence that has been padded with zeros.
      """
      with tf.variable_scope(scope, 'SequenceLength'):
          used = tf.sign(tf.reduce_max(tf.abs(sequence), reduction_indices=[-1]))
          length = tf.cast(tf.reduce_sum(used, reduction_indices=[-1]), tf.int32)
          return length
  
subject: Remove get_sequence_mask in favor of simpler embedding mask