def METHOD_NAME(model, dataloader, mha_sparsity, **kwargs):
    """Get multi-head attention layers pruning configs."""
    # method 1: apply real mha pruning
    mha_pruning_config = [{"pattern": "mha", "target_sparsity": mha_sparsity}]
    # append kwargs to generated config
    for item in mha_pruning_config:
        item.update(kwargs)
    return mha_pruning_config
    # method 2: apply experimental mha pruning
    # from .pattern_analyzer import SelfMHASearcher
    # searcher = SelfMHASearcher(model, dataloader)
    # qkv_pattern, ffn_pattern = searcher.get_head_pattern()
    # qkv_layers, ffn_layers = searcher.search()
    # mha_pruning_config = [
    #     {
    #         "op_names": qkv_layers,
    #         "pattern": qkv_pattern,
    #         "target_sparsity": mha_sparsity,
    #     },
    #     {
    #         "op_names": ffn_layers,
    #         "pattern": ffn_pattern,
    #         "target_sparsity": mha_sparsity,
    #     }
    # ]
    # # append kwargs to generated config
    # for item in mha_pruning_config:
    #     item.update(kwargs)
    # return mha_pruning_config