import tensorflow as tf

                                                                                      step, loss),
                                          target_spectrogram=target,
                                          max_len=target_length)
                    log("Input at step {}: {}".format(step, sequence_to_text(input_seq)))
                
                if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1:
                    # Get current checkpoint state
                    checkpoint_state = tf.train.get_checkpoint_state(save_dir)
                    
                    # Update Projector
                    log("\nSaving Model Character Embeddings visualization..")
                    add_embedding_stats(summary_writer, [model.embedding_table.name],
                                        [char_embedding_meta],
                                        checkpoint_state.model_checkpoint_path)
