def forward(self, x):
        """
        Args:
            input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
                feature map tensor for each feature level in high to low resolution order.
        Returns:
            dict[str->Tensor]:
                mapping from feature map name to FPN feature map tensor
                in high to low resolution order. Returned feature names follow the FPN
                paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
                ["n2", "n3", ..., "n6"].
        """
        bottom_up_features = self.bottom_up(x)
        feats = [bottom_up_features[f] for f in self.in_features]

        for bifpn in self.repeated_bifpn:
             feats = bifpn(feats)

        return dict(zip(self._out_features, feats))

        Args:
            input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
                feature map tensor for each feature level in high to low resolution order.
        Returns:
            dict[str->Tensor]:
                mapping from feature map name to FPN feature map tensor
                in high to low resolution order. Returned feature names follow the FPN
                paper convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
                ["n2", "n3", ..., "n6"].
        
video_chat_with_StableLM/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py