LSTM seq2seq model with attention, main step used for training.

def lstm_seq2seq_internal_attention(inputs, targets, hparams, train,
                                    inputs_length, targets_length):
  """LSTM seq2seq model with attention, main step used for training."""
  with tf.variable_scope("lstm_seq2seq_attention"):
    # Flatten inputs.
    inputs = common_layers.flatten4d3d(inputs)

    # LSTM encoder.
    inputs = tf.reverse_sequence(inputs, inputs_length, seq_axis=1)
    encoder_outputs, final_encoder_state = lstm(
        inputs, inputs_length, hparams, train, "encoder")

    # LSTM decoder with attention.
    shifted_targets = common_layers.shift_right(targets)
    # Add 1 to account for the padding added to the left from shift_right
    targets_length = targets_length + 1
    decoder_outputs = lstm_attention_decoder(
        common_layers.flatten4d3d(shifted_targets), hparams, train, "decoder",
        final_encoder_state, encoder_outputs, inputs_length, targets_length)
    return tf.expand_dims(decoder_outputs, axis=2)