Understanding of implementation of roi_pool and roi_aligh in torchvision

[docs]def roi_pool(
    input: Tensor,
    boxes: Tensor,
    output_size: BroadcastingList2[int],
    spatial_scale: float = 1.0,
) -> Tensor:
    Performs Region of Interest (RoI) Pool operator described in Fast R-CNN

        input (Tensor[N, C, H, W]): input tensor
        boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
            format where the regions will be taken from. If a single Tensor is passed,
            then the first column should contain the batch index. If a list of Tensors
            is passed, then each Tensor will correspond to the boxes for an element i
            in a batch
        output_size (int or Tuple[int, int]): the size of the output after the cropping
            is performed, as (height, width)
        spatial_scale (float): a scaling factor that maps the input coordinates to
            the box coordinates. Default: 1.0

        output (Tensor[K, C, output_size[0], output_size[1]])
    rois = boxes
    output_size = _pair(output_size)
    if not isinstance(rois, torch.Tensor):
        rois = convert_boxes_to_roi_format(rois)
    output, _ = torch.ops.torchvision.roi_pool(input, rois, spatial_scale,
                                               output_size[0], output_size[1])
    return output

here is the implementation of ROI_Pool in torchvision. I can’t understand how it works because I didn’t find the to torch.ops.torchvision.roi_pool. please help me and explain how does this function work.