8b9acc51a5e27378a6b337018480ff9434bbf6f8,open_seq2seq/models/seq2seq.py,Seq2Seq,_build_forward_pass_graph,#Seq2Seq#Any#Any#,114

Before Change

      }
      encoder_output = self.encoder.encode(input_dict=encoder_input)

      tgt_length_eval = tf.cast(1.2 * tf.cast(src_length, tf.float32), tf.int32)
      decoder_input = {
        "encoder_output": encoder_output,
        "tgt_sequence": tgt_sequence if self.mode == "train" else None,
        // when the mode is not "train", replacing correct tgt_length with
        // somewhat increased src_length
        "tgt_length": tgt_length if self.mode == "train" else tgt_length_eval
      }
      decoder_output = self.decoder.decode(input_dict=decoder_input)
      decoder_samples = decoder_output.get("samples", None)

      if self.mode == "train" or self.mode == "eval":

After Change

    See :meth:`models.model.Model._build_forward_pass_graph` for description of
    arguments and return values.
    
    source_tensors = input_tensors["source_tensors"]
    if self.mode == "train" or self.mode == "eval":
      target_tensors = input_tensors["target_tensors"]

    with tf.variable_scope("ForwardPass"):

Instances