def batched_soft_nms(
        boxes, scores, idxs, method, gaussian_sigma, linear_threshold, prune_threshold
):
    """
    Performs soft non-maximum suppression in a batched fashion.

    Each index value correspond to a category, and NMS
    will not be applied between elements of different categories.

    Args:
        boxes (Tensor[N, 4]):
           boxes where NMS will be performed. They
           are expected to be in (x1, y1, x2, y2) format
        scores (Tensor[N]):
           scores for each one of the boxes
        idxs (Tensor[N]):
           indices of the categories for each one of the boxes.
        method (str):
           one of ['gaussian', 'linear', 'hard']
           see paper for details. users encouraged not to use "hard", as this is the
           same nms available elsewhere in detectron2
        gaussian_sigma (float):
           parameter for Gaussian penalty function
        linear_threshold (float):
           iou threshold for applying linear decay. Nt from the paper
           re-used as threshold for standard "hard" nms
        prune_threshold (float):
           boxes with scores below this threshold are pruned at each iteration.
           Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
    Returns:
        tuple(Tensor, Tensor):
            [0]: int64 tensor with the indices of the elements that have been kept
            by Soft NMS, sorted in decreasing order of scores
            [1]: float tensor with the re-scored scores of the elements that were kept
    """
    if boxes.numel() == 0:
        return (
            torch.empty((0,), dtype=torch.int64, device=boxes.device),
            torch.empty((0,), dtype=torch.float32, device=scores.device),
        )
    # strategy: in order to perform NMS independently per class.
    # we add an offset to all the boxes. The offset is dependent
    # only on the class idx, and is large enough so that boxes
    # from different classes do not overlap
    max_coordinate = boxes.max()
    offsets = idxs.to(boxes) * (max_coordinate + 1)
    boxes_for_nms = boxes + offsets[:, None]
    return soft_nms(
        boxes_for_nms, scores, method, gaussian_sigma, linear_threshold, prune_threshold
    )

    Performs soft non-maximum suppression in a batched fashion.

    Each index value correspond to a category, and NMS
    will not be applied between elements of different categories.

    Args:
        boxes (Tensor[N, 4]):
           boxes where NMS will be performed. They
           are expected to be in (x1, y1, x2, y2) format
        scores (Tensor[N]):
           scores for each one of the boxes
        idxs (Tensor[N]):
           indices of the categories for each one of the boxes.
        method (str):
           one of ['gaussian', 'linear', 'hard']
           see paper for details. users encouraged not to use "hard", as this is the
           same nms available elsewhere in detectron2
        gaussian_sigma (float):
           parameter for Gaussian penalty function
        linear_threshold (float):
           iou threshold for applying linear decay. Nt from the paper
           re-used as threshold for standard "hard" nms
        prune_threshold (float):
           boxes with scores below this threshold are pruned at each iteration.
           Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
    Returns:
        tuple(Tensor, Tensor):
            [0]: int64 tensor with the indices of the elements that have been kept
            by Soft NMS, sorted in decreasing order of scores
            [1]: float tensor with the re-scored scores of the elements that were kept
    
video_chat_text/video_chat_with_MOSS/models/grit_src/grit/modeling/soft_nms.py