def forward(self, features: List[torch.Tensor]):
        """
        Args:
            features (list[Tensor]): list of feature maps

        Returns:
            list[Tensor]: A list of L elements.
                Element i is a tensor of shape (N, A, Hi, Wi) representing
                the predicted objectness logits for all anchors. A is the number of cell anchors.
            list[Tensor]: A list of L elements. Element i is a tensor of shape
                (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors
                to proposals.
        """
        pred_objectness_logits = []
        pred_anchor_deltas = []
        for x in features:
            t = self.conv(x)
            pred_objectness_logits.append(self.objectness_logits(t))
            pred_anchor_deltas.append(self.anchor_deltas(t))
        return pred_objectness_logits, pred_anchor_deltas

        Args:
            features (list[Tensor]): list of feature maps

        Returns:
            list[Tensor]: A list of L elements.
                Element i is a tensor of shape (N, A, Hi, Wi) representing
                the predicted objectness logits for all anchors. A is the number of cell anchors.
            list[Tensor]: A list of L elements. Element i is a tensor of shape
                (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors
                to proposals.
        
EVA-02/det/detectron2/modeling/proposal_generator/rpn.py