How to use the mtcnn.first_stage.run_first_stage function in mtcnn

min_length *= m

        factor_count = 0
        while min_length > min_detection_size:
            scales.append(m * factor ** factor_count)
            min_length *= factor
            factor_count += 1

        # STAGE 1

        # it will be returned
        bounding_boxes = []

        # run P-Net on different scales
        for s in scales:
            boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
            bounding_boxes.append(boxes)

        # collect boxes (and offsets, and scores) from different scales
        bounding_boxes = [i for i in bounding_boxes if i is not None]
        bounding_boxes = np.vstack(bounding_boxes)

        keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
        bounding_boxes = bounding_boxes[keep]

        # use offsets predicted by pnet to transform bounding boxes
        bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
        # shape [n_boxes, 5]

        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

factor_count = 0
    while min_length > min_detection_size:
        scales.append(m*factor**factor_count)
        min_length *= factor
        factor_count += 1

    # STAGE 1

    # it will be returned
    bounding_boxes = []

    # run P-Net on different scales
    with torch.no_grad():
        for s in scales:
            boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
            bounding_boxes.append(boxes)

    # collect boxes (and offsets, and scores) from different scales
    bounding_boxes = [i for i in bounding_boxes if i is not None]
    bounding_boxes = np.vstack(bounding_boxes)

    keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
    bounding_boxes = bounding_boxes[keep]

    # use offsets predicted by pnet to transform bounding boxes
    bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
    # shape [n_boxes, 5]

    bounding_boxes = convert_to_square(bounding_boxes)
    bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

min_length *= m

            factor_count = 0
            while min_length > min_detection_size:
                scales.append(m * factor ** factor_count)
                min_length *= factor
                factor_count += 1

            # STAGE 1

            # it will be returned
            bounding_boxes = []

            # run P-Net on different scales
            for s in scales:
                boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0])
                bounding_boxes.append(boxes)

            # collect boxes (and offsets, and scores) from different scales
            bounding_boxes = [i for i in bounding_boxes if i is not None]
            bounding_boxes = np.vstack(bounding_boxes)

            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
            bounding_boxes = bounding_boxes[keep]

            # use offsets predicted by pnet to transform bounding boxes
            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
            # shape [n_boxes, 5]

            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

min_length *= m

        factor_count = 0
        while min_length > min_detection_size:
            scales.append(m * factor ** factor_count)
            min_length *= factor
            factor_count += 1

        # STAGE 1

        # it will be returned
        bounding_boxes = []

        # run P-Net on different scales
        for s in scales:
            boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
            bounding_boxes.append(boxes)

        # collect boxes (and offsets, and scores) from different scales
        bounding_boxes = [i for i in bounding_boxes if i is not None]
        bounding_boxes = np.vstack(bounding_boxes)

        keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
        bounding_boxes = bounding_boxes[keep]

        # use offsets predicted by pnet to transform bounding boxes
        bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
        # shape [n_boxes, 5]

        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

min_length *= m

            factor_count = 0
            while min_length > min_detection_size:
                scales.append(m * factor ** factor_count)
                min_length *= factor
                factor_count += 1

            # STAGE 1

            # it will be returned
            bounding_boxes = []

            # run P-Net on different scales
            for s in scales:
                boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0])
                bounding_boxes.append(boxes)

            # collect boxes (and offsets, and scores) from different scales
            bounding_boxes = [i for i in bounding_boxes if i is not None]
            bounding_boxes = np.vstack(bounding_boxes)

            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
            bounding_boxes = bounding_boxes[keep]

            # use offsets predicted by pnet to transform bounding boxes
            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
            # shape [n_boxes, 5]

            bounding_boxes = convert_to_square(bounding_boxes)
            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])