class_docstr: Basic Sequence2Sequence model with a unidirectional encoder and decoder.
  The last encoder state is used to initialize the decoder and thus both
  must share the same type of RNN cell.
  
  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: ["Basic","Sequence2Sequence","model","with","a","unidirectional","encoder","and","decoder",".","The","last","encoder","state","is","used","to","initialize","the","decoder","and","thus","both","must","share","the","same","type","of","RNN","cell",".","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"]
docstr: Runs decoding in inference mode
docstr_tok: ["Runs","decoding","in","inference","mode"]
code: def _decode_infer(
      self, decoder, bridge, _encoder_output, features, labels
  ):
      """Runs decoding in inference mode"""
      batch_size = self.batch_size(features, labels)
      if self.use_beam_search:
          batch_size = self.params["inference.beam_search.beam_width"]
  
      target_start_id = (
          self.target_vocab_info.special_vocab.SEQUENCE_START
      )
      helper_infer = tf_decode_helper.GreedyEmbeddingHelper(
          embedding=self.target_embedding,
          start_tokens=tf.fill([batch_size], target_start_id),
          end_token=self.target_vocab_info.special_vocab.SEQUENCE_END,
      )
      decoder_initial_state = bridge()
      return decoder(decoder_initial_state, helper_infer)
  
file_path: seq2seq/seq2seq/models/basic_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.contrib.seq2seq import helper
  from seq2seq.models.seq2seq_model import Seq2SeqModel
  from seq2seq.graph_utils import templatemethod
  from seq2seq.models import bridges
label_desc: Use image node_id 6 for calling the BasicSeq2Seq obj's underlying member method code with example usage: obj._decode_infer(decoder, bridge, _encoder_output, features, labels) and returns: decoder