class_docstr: Encode a sequence by averaging the input embeddings.
class_docstr_tok: ["Encode","a","sequence","by","averaging","the","input","embeddings","."]
code: def embed(self, inputs, mask):
      inputs = inputs * tf.expand_dims(mask, -1)
      # counts = tf.reduce_sum(mask, -1, keep_dims=True)
      weights = tf.expand_dims(self.pos_w, 0)
      output = tf.reduce_sum(inputs * weights, axis=1)
      # output = sum_inp / tf.maximum(counts, 1)
      self.add_to_collection("output", output)
      return output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import utils
label_desc: Use image node_id 2 for calling the PositionSumSeqEncoder obj's underlying member method code with example usage: obj.embed(inputs, mask) and returns: output