def METHOD_NAME(self):
    random_array = np.random.random_sample([100, 100, 3]) * 255
    random_array = random_array.astype(np.uint8)
    im = Image.fromarray(random_array)
    os.makedirs("val2017", exist_ok=True)
    im.save("./val2017/test_0.jpg")
    im.save("./val2017/test_1.jpg")
    fake_dict = {
        "info": {
            "description": "COCO 2017 Dataset",
            "url": "http://cocodataset.org",
            "version": "1.0",
            "year": 2017,
            "contributor": "COCO Consortium",
            "date_created": "2017/09/01",
        },
        "licenses": {},
        "images": [
            {"file_name": "test_0.jpg", "height": 100, "width": 100, "id": 0},
            {"file_name": "test_1.jpg", "height": 100, "width": 100, "id": 1},
        ],
        "annotations": [
            {
                "category_id": 18,
                "id": 1767,
                "iscrowd": 0,
                "image_id": 0,
                "bbox": [473.07, 395.93, 38.65, 28.67],
            },
            {
                "category_id": 18,
                "id": 1768,
                "iscrowd": 0,
                "image_id": 1,
                "bbox": [473.07, 395.93, 38.65, 28.67],
            },
            {
                "category_id": 18,
                "id": 1768,
                "iscrowd": 0,
                "image_id": 1,
                "bbox": [473.07, 395.93, 38.65, 28.67],
            },
        ],
        "categories": [{"supercategory": "animal", "id": 18, "name": "dog"}],
    }
    fake_json = json.dumps(fake_dict)
    os.makedirs("annotations", exist_ok=True)
    with open("./annotations/instances_val2017.json", "w") as f:
        f.write(fake_json)
    filters = FILTERS("onnxrt_qlinearops")
    filter = filters["LabelBalanceCOCORaw"](1)
    datasets = Datasets("onnxrt_qlinearops")
    dataset = datasets["COCORaw"]("./", transform=None, filter=filter)
    dataloader = DATALOADERS["onnxrt_qlinearops"](dataset=dataset, batch_size=1)
    for inputs, labels in dataloader:
        self.assertEqual(labels[0].shape[1], 1)
    shutil.rmtree("annotations")
    shutil.rmtree("val2017")