code: def imagetransformer_cifar10_base_dmol():
    
    hparams = image_transformer_base()
    hparams.likelihood = cia.DistributionType.DMOL
    hparams.num_channels = 1
    hparams.bottom["targets"] = modalities.image_channel_compress_targets_bottom
    hparams.top["targets"] = modalities.identity_top
    hparams.num_heads = 8
    hparams.batch_size = 8
    hparams.sampling_method = "random"
    hparams.layer_preprocess_sequence = "n"
    hparams.layer_postprocess_sequence = "da"
    hparams.summarize_grads = True
    hparams.hidden_size = 256
    hparams.filter_size = 512
    hparams.attention_key_channels = 512
    hparams.attention_value_channels = 512
    hparams.num_decoder_layers = 12
    hparams.layer_prepostprocess_dropout = 0.1
    hparams.learning_rate = 0.1
    hparams.layer_preprocess_sequence = "none"
    hparams.layer_postprocess_sequence = "dan"
    hparams.pos = "emb"
    hparams.unconditional = True
    return hparams
docs: Best config for 2.90 bits/dim on CIFAR10 using DMOL.