def convert_boxes_to_pooler_format(box_lists: List[Boxes]):
    """
    Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops
    (see description under Returns).

    Args:
        box_lists (list[Boxes] | list[RotatedBoxes]):
            A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch.

    Returns:
        When input is list[Boxes]:
            A tensor of shape (M, 5), where M is the total number of boxes aggregated over all
            N batch images.
            The 5 columns are (batch index, x0, y0, x1, y1), where batch index
            is the index in [0, N) identifying which batch image the box with corners at
            (x0, y0, x1, y1) comes from.
        When input is list[RotatedBoxes]:
            A tensor of shape (M, 6), where M is the total number of boxes aggregated over all
            N batch images.
            The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees),
            where batch index is the index in [0, N) identifying which batch image the
            rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from.
    """
    boxes = torch.cat([x.tensor for x in box_lists], dim=0)
    # __len__ returns Tensor in tracing.
    sizes = shapes_to_tensor([x.__len__() for x in box_lists], device=boxes.device)
    indices = torch.repeat_interleave(
        torch.arange(len(box_lists), dtype=boxes.dtype, device=boxes.device), sizes
    )
    return cat([indices[:, None], boxes], dim=1)

    Convert all boxes in `box_lists` to the low-level format used by ROI pooling ops
    (see description under Returns).

    Args:
        box_lists (list[Boxes] | list[RotatedBoxes]):
            A list of N Boxes or N RotatedBoxes, where N is the number of images in the batch.

    Returns:
        When input is list[Boxes]:
            A tensor of shape (M, 5), where M is the total number of boxes aggregated over all
            N batch images.
            The 5 columns are (batch index, x0, y0, x1, y1), where batch index
            is the index in [0, N) identifying which batch image the box with corners at
            (x0, y0, x1, y1) comes from.
        When input is list[RotatedBoxes]:
            A tensor of shape (M, 6), where M is the total number of boxes aggregated over all
            N batch images.
            The 6 columns are (batch index, x_ctr, y_ctr, width, height, angle_degrees),
            where batch index is the index in [0, N) identifying which batch image the
            rotated box (x_ctr, y_ctr, width, height, angle_degrees) comes from.
    
video_chat_with_ChatGPT/models/grit_src/third_party/CenterNet2/detectron2/modeling/poolers.py