def METHOD_NAME(self, raw, identifiers, frame_meta):
    result = []
    predictions_batch = self._extract_predictions(raw, frame_meta)
    if not self.outpus_verified:
        self.select_output_blob(predictions_batch)
    hm_batch = predictions_batch[self.center_heatmap_out]
    wh_batch = predictions_batch[self.width_height_out]
    reg_batch = predictions_batch[self.regression_out]
    for identifier, heat, wh, reg, meta in zip(identifiers, hm_batch, wh_batch, reg_batch, frame_meta):
        heat = np.exp(heat)/(1 + np.exp(heat))
        height, width = heat.shape[1:3]
        heat = self._nms(heat)
        scores, inds, clses, ys, xs = self._topk(heat, K=100)
        reg = self._tranpose_and_gather_feat(reg, inds)
        num_predictions = 100
        reg = reg.reshape((num_predictions, 2))
        xs = xs.reshape((num_predictions, 1)) + reg[:, 0:1]
        ys = ys.reshape((num_predictions, 1)) + reg[:, 1:2]
        wh = self._tranpose_and_gather_feat(wh, inds)
        wh = wh.reshape((num_predictions, 2))
        clses = clses.reshape((num_predictions, 1)).astype(float)
        scores = scores.reshape((num_predictions, 1))
        bboxes = np.concatenate((xs - wh[..., 0:1] / 2,
                                 ys - wh[..., 1:2] / 2,
                                 xs + wh[..., 0:1] / 2,
                                 ys + wh[..., 1:2] / 2), axis=1)
        detections = np.concatenate((bboxes, scores, clses), axis=1)
        im_size = meta.get('image_size')
        scale = max(im_size)
        center = np.array(im_size[:2])/2.0
        dets = self._transform(detections, np.flip(center, 0), scale, height, width)
        x_min, y_min, x_max, y_max, scores, classes = dets.transpose(1, 0)
        result.append(DetectionPrediction(identifier, classes, scores, x_min, y_min, x_max, y_max))
    return result