def METHOD_NAME(self,
            feats: Tuple[Tensor],
            batch_data_samples: OptSampleList,
            test_cfg: ConfigType = {}) -> Predictions:
    """Predict results from outputs."""
    if test_cfg.get('flip_test', False):
        # TTA: flip test -> feats = [orig, flipped]
        assert isinstance(feats, list) and len(feats) == 2
        flip_indices = batch_data_samples[0].metainfo['flip_indices']
        input_size = batch_data_samples[0].metainfo['input_size']
        _feats, _feats_flip = feats
        _batch_coords = self.forward(_feats)
        _batch_coords_flip = flip_coordinates(
            self.forward(_feats_flip),
            flip_indices=flip_indices,
            shift_coords=test_cfg.get('shift_coords', True),
            input_size=input_size)
        batch_coords = (_batch_coords + _batch_coords_flip) * 0.5
    else:
        batch_coords = self.forward(feats)  # (B, K, D)
    batch_coords.unsqueeze_(dim=1)  # (B, N, K, D)
    preds = self.decode(batch_coords)
    return preds