def METHOD_NAME():
    '''Embed DB chunks.
    Store chunks in blocks on disk. These blocks will later be merged into
    a single dataset for training the index.
    '''
    args = get_retro_args()
    merged_train_data_path = get_training_data_merged_path()
    if os.path.exists(merged_train_data_path):
        return
    # Get db dataset.
    gpt_dataset = get_merged_sampled_dataset()
    text_dataset = GPTToTextDataset(gpt_dataset)
    # Embed dataset.
    embedder = DiskDataParallelBertEmbedder(args.retro_bert_batch_size,
                                            args.retro_bert_max_chunk_length,
                                            args.retro_block_size,
                                            args.bert_embedder_type)
    embedder.embed_text_dataset("index",
                                get_training_data_block_dir(),
                                text_dataset)
    # Merge embeddings.
    merge_embedding_blocks()