def create_score_dictionary_from_metrics(weights: list[float], *metrics: list[float]) -> dict[int, int]:
    """Combine multiple metrics into a single weighted score dictionary.

    Each metric is a list of values (smaller = better).
    The total score for each index is the weighted sum of its values
    across all metrics:

        score[index] = Σ (value * weight)

    Args:
        weights: A list of weights, one per metric. Larger weight = more influence.
        *metrics: Lists of values (one list per metric, aligned by index).

    Returns:
        A dictionary mapping each index to its combined weighted score.

    """
    if len(weights) != len(metrics):
        raise ValueError("Number of weights must match number of metrics")

    combined: dict[int, float] = {}

    for weight, metric in zip(weights, metrics):
        for idx, value in enumerate(metric):
            combined[idx] = combined.get(idx, 0) + value * weight

    return combined

if len(weights) != len(metrics):
    raise ValueError("Number of weights must match number of metrics")

combined: dict[int, float] = {}

for weight, metric in zip(weights, metrics):
    for idx, value in enumerate(metric):
        combined[idx] = combined.get(idx, 0) + value * weight

weights = choose_weights(runtime=3, diff=1)

runtime_norm = normalize(runtimes_list)
diffs_norm = normalize(diff_lens_list)
score_dict = create_score_dictionary_from_metrics(weights, runtime_norm, diffs_norm)

min_key = min(score_dict, key=score_dict.get)
best_optimization = valid_candidates_with_shorter_code[min_key]