//  [python]
def test_batched_coco_detection_annotations(self):
        image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))

        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
            target = json.loads(f.read())

        annotations_0 = {"image_id": 39769, "annotations": target}
        annotations_1 = {"image_id": 39769, "annotations": target}

        # Adjust the bounding boxes for the resized image
        w_0, h_0 = image_0.size
        w_1, h_1 = image_1.size
        for i in range(len(annotations_1["annotations"])):
            coords = annotations_1["annotations"][i]["bbox"]
            new_bbox = [
                coords[0] * w_1 / w_0,
                coords[1] * h_1 / h_0,
                coords[2] * w_1 / w_0,
                coords[3] * h_1 / h_0,
            ]
            annotations_1["annotations"][i]["bbox"] = new_bbox

        images = [image_0, image_1]
        annotations = [annotations_0, annotations_1]

        image_processing = DeformableDetrFeatureExtractor()
        encoding = image_processing(
            images=images,
            annotations=annotations,
            return_segmentation_masks=True,
            return_tensors="pt",  # do_convert_annotations=True
        )

        # Check the pixel values have been padded
        postprocessed_height, postprocessed_width = 800, 1066
        expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
        self.assertEqual(encoding["pixel_values"].shape, expected_shape)

        # Check the bounding boxes have been adjusted for padded images
        self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
        self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
        expected_boxes_0 = torch.tensor(
            [
                [0.6879, 0.4609, 0.0755, 0.3691],
                [0.2118, 0.3359, 0.2601, 0.1566],
                [0.5011, 0.5000, 0.9979, 1.0000],
                [0.5010, 0.5020, 0.9979, 0.9959],
                [0.3284, 0.5944, 0.5884, 0.8112],
                [0.8394, 0.5445, 0.3213, 0.9110],
            ]
        )
        expected_boxes_1 = torch.tensor(
            [
                [0.4130, 0.2765, 0.0453, 0.2215],
                [0.1272, 0.2016, 0.1561, 0.0940],
                [0.3757, 0.4933, 0.7488, 0.9865],
                [0.3759, 0.5002, 0.7492, 0.9955],
                [0.1971, 0.5456, 0.3532, 0.8646],
                [0.5790, 0.4115, 0.3430, 0.7161],
            ]
        )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
        self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))

        # Check the masks have also been padded
        self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
        self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))

        # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
        # format and not in the range [0, 1]
        encoding = image_processing(
            images=images,
            annotations=annotations,
            return_segmentation_masks=True,
            do_convert_annotations=False,
            return_tensors="pt",
        )
        self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
        self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
        # Convert to absolute coordinates
        unnormalized_boxes_0 = torch.vstack(
            [
                expected_boxes_0[:, 0] * postprocessed_width,
                expected_boxes_0[:, 1] * postprocessed_height,
                expected_boxes_0[:, 2] * postprocessed_width,
                expected_boxes_0[:, 3] * postprocessed_height,
            ]
        ).T
        unnormalized_boxes_1 = torch.vstack(
            [
                expected_boxes_1[:, 0] * postprocessed_width,
                expected_boxes_1[:, 1] * postprocessed_height,
                expected_boxes_1[:, 2] * postprocessed_width,
                expected_boxes_1[:, 3] * postprocessed_height,
            ]
        ).T
        # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
        expected_boxes_0 = torch.vstack(
            [
                unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
                unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
                unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
                unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
            ]
        ).T
        expected_boxes_1 = torch.vstack(
            [
                unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
                unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
                unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
                unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
            ]
        ).T
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
        self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))