def METHOD_NAME(self, mock_load):
    mock_load.side_effect = lambda *x, **y: None
    seed = 1
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    # init from alias
    self.inferencer = KIEInferencer('SDMGR')
    self.inferencer_novisual = KIEInferencer(
        'sdmgr_novisual_60e_wildreceipt')
    with open('tests/data/kie_toy_dataset/wildreceipt/data.txt', 'r') as f:
        annos = [json.loads(anno) for anno in f.readlines()]
    self.data_novisual = []
    self.data_img_str = []
    self.data_img_ndarray = []
    self.data_img_woshape = []
    for anno in annos:
        datum_novisual = dict(img_shape=(anno['height'], anno['width']))
        datum_novisual['instances'] = []
        for ann in anno['annotations']:
            instance = {}
            instance['bbox'] = poly2bbox(
                np.array(ann['box'], dtype=np.float32))
            instance['text'] = ann['text']
            datum_novisual['instances'].append(instance)
        self.data_novisual.append(datum_novisual)
        datum_img_str = deepcopy(datum_novisual)
        datum_img_str['img'] = anno['file_name']
        self.data_img_str.append(datum_img_str)
        datum_img_ndarray = deepcopy(datum_novisual)
        datum_img_ndarray['img'] = mmcv.imread(anno['file_name'])
        self.data_img_ndarray.append(datum_img_ndarray)
        datum_img_woshape = deepcopy(datum_img_str)
        del datum_img_woshape['img_shape']
        self.data_img_woshape.append(datum_img_woshape)