def METHOD_NAME(tmpdir):
    rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
    os.makedirs(tmpdir / "train_folder", exist_ok=True)
    os.makedirs(tmpdir / "predict_folder", exist_ok=True)
    train_folder = tmpdir / "train_folder"
    train_ann_file = tmpdir / "train_annotations.json"
    predict_folder = tmpdir / "predict_folder"
    _ = [rand_image.save(str(train_folder / f"image_{i}.png")) for i in range(1, 4)]
    _ = [rand_image.save(str(predict_folder / f"predict_image_{i}.png")) for i in range(1, 4)]
    annotations = {
        "annotations": [
            {
                "area": 50,
                "bbox": [10, 20, 5, 10],
                "num_keypoints": 2,
                "keypoints": [10, 15, 2, 20, 30, 2],
                "category_id": 1,
                "id": 1,
                "image_id": 1,
                "iscrowd": 0,
            },
            {
                "area": 100,
                "bbox": [20, 30, 10, 10],
                "num_keypoints": 2,
                "keypoints": [20, 30, 2, 30, 40, 2],
                "category_id": 2,
                "id": 2,
                "image_id": 2,
                "iscrowd": 0,
            },
            {
                "area": 125,
                "bbox": [10, 20, 5, 25],
                "num_keypoints": 2,
                "keypoints": [10, 15, 2, 20, 45, 2],
                "category_id": 1,
                "id": 3,
                "image_id": 3,
                "iscrowd": 0,
            },
        ],
        "categories": [
            {"id": 1, "name": "cat", "supercategory": "cat", "keypoints": ["left ear", "right ear"]},
            {"id": 2, "name": "dog", "supercategory": "dog", "keypoints": ["left ear", "right ear"]},
        ],
        "images": [
            {"file_name": "image_1.png", "height": 64, "width": 64, "id": 1},
            {"file_name": "image_2.png", "height": 64, "width": 64, "id": 2},
            {"file_name": "image_3.png", "height": 64, "width": 64, "id": 3},
        ],
    }
    with open(train_ann_file, "w") as annotation_file:
        json.dump(annotations, annotation_file)
    return COCODataConfig(train_folder, train_ann_file, predict_folder)