def METHOD_NAME(self, path, preds, im, im0s):
    results = []
    for i, pred in enumerate(preds):
        pred = METHOD_NAME(
            pred.unsqueeze(0),  # YOLOX postprocessor expects 3D arary
            1,
            conf_thre=0.1,
            nms_thre=0.45,
            class_agnostic=True
        )[0]
        if pred is None:
            pred = torch.empty((0, 6))
            r = Results(
                path=path,
                boxes=pred,
                orig_img=im0s[i],
                names=self.names
            )
            results.append(r)
        else:
            # (x, y, x, y, conf, obj, cls) --> (x, y, x, y, conf, cls)
            pred[:, 4] = pred[:, 4] * pred[:, 5]
            pred = pred[:, [0, 1, 2, 3, 4, 6]]
            pred[:, :4] = ops.scale_boxes(im.shape[2:], pred[:, :4], im0s[i].shape)
            # filter boxes by classes
            if self.args.classes:
                pred = pred[torch.isin(pred[:, 5].cpu(), torch.as_tensor(self.args.classes))]
            r = Results(
                path=path,
                boxes=pred,
                orig_img=im0s[i],
                names=self.names
            )
        results.append(r)
    return results