def METHOD_NAME(self, raw, identifiers=None, frame_meta=None):
    if not self.outputs_verified:
        self.select_output_blob(raw)
    total_boxes_batch = self._extract_predictions(raw, frame_meta)
    results = []
    for total_boxes, identifier in zip(total_boxes_batch, identifiers):
        if np.size(total_boxes) == 0:
            results.append(DetectionPrediction(identifier, [], [], [], [], [], []))
            continue
        pick = self.nms(total_boxes, 0.7, 'Union')
        total_boxes = total_boxes[pick]
        regh = total_boxes[:, 3] - total_boxes[:, 1]
        regw = total_boxes[:, 2] - total_boxes[:, 0]
        x_mins = total_boxes[:, 0] + total_boxes[:, 5] * regw
        y_mins = total_boxes[:, 1] + total_boxes[:, 6] * regh
        x_maxs = total_boxes[:, 2] + total_boxes[:, 7] * regw
        y_maxs = total_boxes[:, 3] + total_boxes[:, 8] * regh
        scores = total_boxes[:, 4]
        results.append(
            DetectionPrediction(identifier, np.full_like(scores, 1), scores, x_mins, y_mins, x_maxs, y_maxs)
        )
    return results