function compute_segments(
    mask_probs,
    pred_scores,
    pred_labels,
    mask_threshold,
    overlap_mask_area_threshold,
    label_ids_to_fuse = null,
    target_size = null,
) {
    const [height, width] = target_size ?? mask_probs[0].dims;

    const segmentation = new Tensor(
        'int32',
        new Int32Array(height * width),
        [height, width]
    );
    const segments = [];

    // 1. If target_size is not null, we need to resize the masks to the target size
    if (target_size !== null) {
        // resize the masks to the target size
        for (let i = 0; i < mask_probs.length; ++i) {
            mask_probs[i] = interpolate(mask_probs[i], target_size, 'bilinear', false);
        }
    }

    // 2. Weigh each mask by its prediction score
    // NOTE: `mask_probs` is updated in-place
    // 
    // Temporary storage for the best label/scores for each pixel ([height, width]):
    const mask_labels = new Int32Array(mask_probs[0].data.length);
    const bestScores = new Float32Array(mask_probs[0].data.length);

    for (let i = 0; i < mask_probs.length; ++i) {
        let score = pred_scores[i];

        const mask_probs_i_data = mask_probs[i].data;

        for (let j = 0; j < mask_probs_i_data.length; ++j) {
            mask_probs_i_data[j] *= score
            if (mask_probs_i_data[j] > bestScores[j]) {
                mask_labels[j] = i;
                bestScores[j] = mask_probs_i_data[j];
            }
        }
    }

    let current_segment_id = 0;

    // let stuff_memory_list = {}
    const segmentation_data = segmentation.data;
    for (let k = 0; k < pred_labels.length; ++k) {
        const pred_class = pred_labels[k];

        // TODO add `should_fuse`
        // let should_fuse = pred_class in label_ids_to_fuse

        // Check if mask exists and large enough to be a segment
        const [mask_exists, mask_k] = check_segment_validity(
            mask_labels,
            mask_probs,
            k,
            mask_threshold,
            overlap_mask_area_threshold
        )

        if (!mask_exists) {
            // Nothing to see here
            continue;
        }

        // TODO
        // if (pred_class in stuff_memory_list) {
        //     current_segment_id = stuff_memory_list[pred_class]
        // } else {
        //     current_segment_id += 1;
        // }
        ++current_segment_id;


        // Add current object segment to final segmentation map
        for (const index of mask_k) {
            segmentation_data[index] = current_segment_id;
        }

        segments.push({
            id: current_segment_id,
            label_id: pred_class,
            // was_fused: should_fuse, TODO
            score: pred_scores[k],
        })

        // TODO
        // if(should_fuse){
        //     stuff_memory_list[pred_class] = current_segment_id
        // }
    }

    return [segmentation, segments];
}
Computes the segments.
@param {Tensor[]} mask_probs The mask probabilities.
@param {number[]} pred_scores The predicted scores.
@param {number[]} pred_labels The predicted labels.
@param {number} mask_threshold The mask threshold.
@param {number} overlap_mask_area_threshold The overlap mask area threshold.
@param {Set<number>} label_ids_to_fuse The label ids to fuse.
@param {number[]} target_size The target size of the image.
@returns {[Tensor, Array<{id: number, label_id: number, score: number}>]} The computed segments.
@private