def forward(self, x):
        """
        Args:
            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.

        Returns:
            dict[str->Tensor]:
                mapping from feature map name to pyramid feature map tensor
                in high to low resolution order. Returned feature names follow the FPN
                convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
                ["p2", "p3", ..., "p6"].
        """
        bottom_up_features = self.net(x)
        features = bottom_up_features[self.in_feature]
        results = []

        for stage in self.stages:
            results.append(stage(features))

        if self.top_block is not None:
            if self.top_block.in_feature in bottom_up_features:
                top_block_in_feature = bottom_up_features[self.top_block.in_feature]
            else:
                top_block_in_feature = results[self._out_features.index(self.top_block.in_feature)]
            results.extend(self.top_block(top_block_in_feature))
        assert len(self._out_features) == len(results)
        return {f: res for f, res in zip(self._out_features, results)}

        Args:
            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.

        Returns:
            dict[str->Tensor]:
                mapping from feature map name to pyramid feature map tensor
                in high to low resolution order. Returned feature names follow the FPN
                convention: "p<stage>", where stage has stride = 2 ** stage e.g.,
                ["p2", "p3", ..., "p6"].
        
EVA-01/det/detectron2/modeling/backbone/vit.py