def truth_ratio(model, **kwargs):
    """Compute the truth ratio, aggregating false/true scores, and
    return the aggregated value."""

    # Forget data: It is better if false and true are equally likely,
    # i.e., tr=false/true is closest to 1.
    def closer_to_1_better(arr):
        return np.mean(np.minimum(arr, 1 / (arr + 1e-10)))

    # Non-forget data: It is better if tr=false/true is lower, i.e.,
    # 1-tr is higher.
    def true_better(arr):
        return np.mean(np.maximum(0, 1 - arr))

    if kwargs["aggregator"] == "closer_to_1_better":
        aggregator = closer_to_1_better
    elif kwargs["aggregator"] == "true_better":
        aggregator = true_better
    else:
        raise ValueError(f"Invalid truth ratio aggregator: {kwargs['aggregator']}")

    correct_answer_results = kwargs["pre_compute"]["correct"]["value_by_index"]
    wrong_answer_results = kwargs["pre_compute"]["wrong"]["value_by_index"]

    correct_indices = list(correct_answer_results.keys())
    wrong_indices = list(wrong_answer_results.keys())
    assert correct_indices == wrong_indices

    # Filter out None values from both correct and wrong answers
    filtered_indices = [
        idx
        for idx in correct_indices
        if correct_answer_results[idx] is not None
        and wrong_answer_results[idx] is not None
    ]
    correct_avg_losses = [
        correct_answer_results[idx]["avg_loss"] for idx in filtered_indices
    ]
    wrong_avg_losses = [
        wrong_answer_results[idx]["avg_loss"] for idx in filtered_indices
    ]

    correct_avg_losses = aggregate_to_1D(np.array(correct_avg_losses))
    wrong_avg_losses = aggregate_to_1D(np.array(wrong_avg_losses))

    correct_prob = np.exp(-correct_avg_losses)
    wrong_prob = np.exp(-wrong_avg_losses)

    truth_ratios = wrong_prob / (correct_prob + 1e-10)
    value_by_index = dict(
        zip(correct_indices, [{"score": val} for val in truth_ratios])
    )
    truth_ratio_stats = np.array([evals["score"] for evals in value_by_index.values()])
    forget_tr_avg = aggregator(truth_ratio_stats)
    return {"agg_value": forget_tr_avg, "value_by_index": value_by_index}

    truth_ratios = wrong_prob / (correct_prob + 1e-10)
    value_by_index = dict(
        zip(correct_indices, [{"score": val} for val in truth_ratios])
    )
    truth_ratio_stats = np.array([evals["score"] for evals in value_by_index.values()])
    forget_tr_avg = aggregator(truth_ratio_stats)

    truth_ratios = correct_prob / (correct_prob + wrong_prob + 1e-10)
    # Store per-index scores
    value_by_index = dict(
        zip(correct_indices, [{"score": val} for val in truth_ratios])
    )

    # Aggregate: mean of all TRs (higher is better)
    truth_ratio_stats = np.array([evals["score"] for evals in value_by_index.values()])
    forget_tr_avg = np.mean(truth_ratio_stats)