code: def transformer_cifar10_memory_v0():
    
    hparams = transformer_wikitext103_l4k_memory_v0()
  
    hparams.num_hidden_layers = 6
  
    hparams.max_length = 32 * 32 * 3
    hparams.split_targets_chunk_length = 64 * 3
    hparams.split_targets_max_chunks = int(
        hparams.max_length / hparams.split_targets_chunk_length)
    hparams.num_memory_items = 128 * 3
  
    # Since this is an image problem, batch size refers to examples (not tokens)
    target_images_per_batch = 4
    hparams.batch_size = int(target_images_per_batch * (
        hparams.max_length / hparams.split_targets_chunk_length))
  
    # The recurrent memory needs to know the actual batch size (in sequences)
    hparams.recurrent_memory_batch_size = hparams.batch_size
  
    hparams.max_relative_position = (
        hparams.num_memory_items + hparams.split_targets_chunk_length)
  
    return hparams
docs: HParams for training image_cifar10_plain_gen_flat_rev with memory.