def _forward(self,
                 inputs: Tensor,
                 data_samples: OptSampleList = None
                 ) -> Union[Tensor, Tuple[Tensor]]:
        """Network forward process. Usually includes backbone, neck and head
        forward without any post-processing.

        Args:
            inputs (Tensor): Inputs with shape (N, K, C, T).

        Returns:
            Union[Tensor | Tuple[Tensor]]: forward output of the network.
        """
        feats = self.extract_feat(inputs)

        if self.with_traj:
            # forward with trajectory model
            x, traj_x = feats
            if self.with_head:
                x = self.head.forward(x)

            traj_x = self.traj_head.forward(traj_x)
            return x, traj_x
        else:
            # forward without trajectory model
            x = feats
            if self.with_head:
                x = self.head.forward(x)
            return x
Network forward process. Usually includes backbone, neck and head
        forward without any post-processing.

        Args:
            inputs (Tensor): Inputs with shape (N, K, C, T).

        Returns:
            Union[Tensor | Tuple[Tensor]]: forward output of the network.
        
mmpose/mmpose/models/pose_estimators/pose_lifter.py