class_docstr: Sequence2Sequence model with attention mechanism.
  
  Args:
    source_vocab_info: An instance of `VocabInfo`
      for the source vocabulary
    target_vocab_info: An instance of `VocabInfo`
      for the target vocabulary
    params: A dictionary of hyperparameters
class_docstr_tok: ["Sequence2Sequence","model","with","attention","mechanism",".","Args",":","source_vocab_info",":","An","instance","of","`","VocabInfo","`","for","the","source","vocabulary","target_vocab_info",":","An","instance","of","`","VocabInfo","`","for","the","target","vocabulary","params",":","A","dictionary","of","hyperparameters"]
code: def default_params():
      params = BasicSeq2Seq.default_params().copy()
      params.update(
          {
              "attention.class": "AttentionLayerBahdanau",
              "attention.params": {},  # Arbitrary attention layer parameters
              "bridge.class": "seq2seq.models.bridges.ZeroBridge",
              "encoder.class": "seq2seq.encoders.BidirectionalRNNEncoder",
              "encoder.params": {},  # Arbitrary parameters for the encoder
              "decoder.class": "seq2seq.decoders.AttentionDecoder",
              "decoder.params": {},  # Arbitrary parameters for the decoder
          }
      )
      return params
  
file_path: seq2seq/seq2seq/models/attention_seq2seq.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from pydoc import locate
  import tensorflow
  from seq2seq import decoders
  from seq2seq.models.basic_seq2seq import BasicSeq2Seq
label_desc: Use image node_id 2 for calling the AttentionSeq2Seq obj's underlying member method code with example usage: obj.default_params() and returns: params