def __call__(self, dataset_dict):
        """
        Args:
            dict: a dict in standard model input format. See tutorials for details.

        Returns:
            list[dict]:
                a list of dicts, which contain augmented version of the input image.
                The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
                Each dict has field "transforms" which is a TransformList,
                containing the transforms that are used to generate this image.
        """
        numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy()
        shape = numpy_image.shape
        orig_shape = (dataset_dict["height"], dataset_dict["width"])
        if shape[:2] != orig_shape:
            # It transforms the "original" image in the dataset to the input image
            pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])
        else:
            pre_tfm = NoOpTransform()

        # Create all combinations of augmentations to use
        aug_candidates = []  # each element is a list[Augmentation]
        for min_size in self.min_sizes:
            resize = ResizeShortestEdge(min_size, self.max_size)
            aug_candidates.append([resize])  # resize only
            if self.flip:
                flip = RandomFlip(prob=1.0)
                aug_candidates.append([resize, flip])  # resize + flip

        # Apply all the augmentations
        ret = []
        for aug in aug_candidates:
            new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))
            torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1)))

            dic = copy.deepcopy(dataset_dict)
            dic["transforms"] = pre_tfm + tfms
            dic["image"] = torch_image
            ret.append(dic)
        return ret

        Args:
            dict: a dict in standard model input format. See tutorials for details.

        Returns:
            list[dict]:
                a list of dicts, which contain augmented version of the input image.
                The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
                Each dict has field "transforms" which is a TransformList,
                containing the transforms that are used to generate this image.
        
video_chat_with_MOSS/models/grit_src/third_party/CenterNet2/detectron2/modeling/test_time_augmentation.py