def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
    """ ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
    Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
    Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock, **kwargs)
    model = _create_vision_transformer('vit_small_patch16_18x2_224', pretrained=pretrained, **model_kwargs)
    return model
 ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
    Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
    Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
    
training/data_efficiency/vit_finetuning/models/vit.py