18a12b023f394b102ac6cafa1b935055c6f8d849,ludwig/models/modules/sequence_decoders.py,SequenceGeneratorDecoder,call,#SequenceGeneratorDecoder#Any#Any#Any#,278

Before Change

        print(">>>>>>batch_size", batch_size)

        // if encode end state exists use it, otherwise set up zero end state
        if "encoder_end_state" in encoder_outputs:
            encoder_end_state = encoder_outputs["encoder_end_state"]
        else:
            encoder_end_state = [

After Change

                tf.zeros([batch_size, self.rnn_units], tf.float32),
                tf.zeros([batch_size, self.rnn_units], tf.float32)
            ]
        elif self.cell_type in {"rnn", "gru"} and encoder_end_state is None:
            encoder_end_state = tf.zeros([batch_size, self.rnn_units], tf.float32)

        if training:

Instances