def METHOD_NAME(tf_weights: dict, cfg_updates: dict) -> PegasusForConditionalGeneration:
    cfg_kwargs = DEFAULTS.copy()
    cfg_kwargs.update(cfg_updates)
    cfg = PegasusConfig(**cfg_kwargs)
    torch_model = PegasusForConditionalGeneration(cfg)
    sd = torch_model.model.state_dict()
    mapping = {}
    for k, v in tf_weights.items():
        new_k = rename_state_dict_key(k)
        if new_k not in sd:
            raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})")
        if "dense" in k or "proj" in new_k:
            v = v.T
        mapping[new_k] = torch.tensor(v, dtype=sd[new_k].dtype)
        assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
    # make sure embedding.padding_idx is respected
    mapping["shared.weight"][cfg.pad_token_id] = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1])
    mapping["encoder.embed_tokens.weight"] = mapping["shared.weight"]
    mapping["decoder.embed_tokens.weight"] = mapping["shared.weight"]
    empty_biases = {k: torch.zeros_like(v) for k, v in sd.items() if k.endswith("bias") and k not in mapping}
    mapping.update(**empty_biases)
    missing, extra = torch_model.model.load_state_dict(mapping, strict=False)
    unexpected_missing = [
        k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
    ]
    assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
    assert extra == [], f"no matches found for the following tf keys {extra}"
    return torch_model