code: def imagetransformerpp_sep_channels_8l_8h():
    
    hparams = imagetransformer_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 = 4
    hparams.attention_key_channels = hparams.attention_value_channels = 0
    hparams.hidden_size = 512
    hparams.filter_size = 512
    hparams.num_hidden_layers = 8
    hparams.sampling_method = "random"
    hparams.layer_preprocess_sequence = "n"
    hparams.layer_postprocess_sequence = "da"
    hparams.summarize_grads = True
    hparams.learning_rate = 0.1
    return hparams
docs: separate rgb embeddings.