code: def img2img_transformer_base():
    
    hparams = image_transformer2d_base()
    # learning related flags
    hparams.layer_preprocess_sequence = "n"
    hparams.layer_postprocess_sequence = "da"
    # This version seems to benefit from a higher learning rate.
    hparams.learning_rate = 0.2
    hparams.layer_prepostprocess_dropout = 0.1
    hparams.learning_rate_warmup_steps = 12000
    hparams.filter_size = 2048
    hparams.num_encoder_layers = 4
    hparams.num_decoder_layers = 8
    hparams.block_length = 256
    hparams.block_width = 256
    hparams.dec_attention_type = cia.AttentionType.LOCAL_1D
    hparams.block_raster_scan = False
    return hparams
docs: Base params for local1d attention.