def forward(self, batched_inputs):
        """
        Args:
            Same as in :class:`GeneralizedRCNN.forward`

        Returns:
            list[dict]:
                Each dict is the output for one input image.
                The dict contains one key "proposals" whose value is a
                :class:`Instances` with keys "proposal_boxes" and "objectness_logits".
        """
        images = [x["image"].to(self.device) for x in batched_inputs]
        images = [(x - self.pixel_mean) / self.pixel_std for x in images]
        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
        features = self.backbone(images.tensor)

        if "instances" in batched_inputs[0]:
            gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
        elif "targets" in batched_inputs[0]:
            log_first_n(
                logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10
            )
            gt_instances = [x["targets"].to(self.device) for x in batched_inputs]
        else:
            gt_instances = None
        proposals, proposal_losses = self.proposal_generator(images, features, gt_instances)
        # In training, the proposals are not useful at all but we generate them anyway.
        # This makes RPN-only models about 5% slower.
        if self.training:
            return proposal_losses

        processed_results = []
        for results_per_image, input_per_image, image_size in zip(
            proposals, batched_inputs, images.image_sizes
        ):
            height = input_per_image.get("height", image_size[0])
            width = input_per_image.get("width", image_size[1])
            r = detector_postprocess(results_per_image, height, width)
            processed_results.append({"proposals": r})
        return processed_results

        Args:
            Same as in :class:`GeneralizedRCNN.forward`

        Returns:
            list[dict]:
                Each dict is the output for one input image.
                The dict contains one key "proposals" whose value is a
                :class:`Instances` with keys "proposal_boxes" and "objectness_logits".
        
video_chat_text/video_chat_with_StableLM/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/rcnn.py