def get_from_to_our_keys(model_name: str) -> Dict[str, str]:
    """
    Returns a dictionary that maps from original model's key -> our implementation's keys
    """

    # create our model (with small weights)
    our_config = RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[8, 8, 8, 8], groups_width=8)
    if "in1k" in model_name:
        our_model = RegNetForImageClassification(our_config)
    else:
        our_model = RegNetModel(our_config)
    # create from model (with small weights)
    from_model = FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
    )

    with torch.no_grad():
        from_model = from_model.eval()
        our_model = our_model.eval()

        x = torch.randn((1, 3, 32, 32))
        # trace both
        dest_tracker = Tracker(our_model)
        dest_traced = dest_tracker(x).parametrized

        pprint(dest_tracker.name2module)
        src_tracker = Tracker(from_model)
        src_traced = src_tracker(x).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(dict_with_modules):
        params_dict = OrderedDict()
        for name, module in dict_with_modules.items():
            for param_name, param in module.state_dict().items():
                params_dict[f"{name}.{param_name}"] = param
        return params_dict

    from_to_ours_keys = {}

    src_state_dict = to_params_dict(src_traced)
    dst_state_dict = to_params_dict(dest_traced)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(), dst_state_dict.items()):
        from_to_ours_keys[src_key] = dest_key
        logger.info(f"{src_key} -> {dest_key}")
    # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
    if "in1k" in model_name:
        from_to_ours_keys["0.clf.0.weight"] = "classifier.1.weight"
        from_to_ours_keys["0.clf.0.bias"] = "classifier.1.bias"

    return from_to_ours_keys

    Returns a dictionary that maps from original model's key -> our implementation's keys
    
dpkd/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py