def METHOD_NAME(self, model_file: bytes):
    interp = tf.lite.Interpreter(model_path=model_file)
    interp.allocate_tensors()
    for idx, value in enumerate(self.inputs):
        new_value = self.transform_input(
            self.data_pre_process(value['data']), "float32", "CPU")[0]
        interp.set_tensor(value["index"], new_value)
        if self.cfg['compile_opt']['preprocess'] and not test_utils.in_ci():
            dump_bin_file(os.path.join(self.case_dir, f'frame_input_{idx}.bin'), new_value)
            dump_txt_file(os.path.join(self.case_dir, f'frame_input_{idx}.txt'), new_value)
    interp.invoke()
    i = 0
    results = []
    for output in self.outputs:
        data = interp.get_tensor(output['index'])
        results.append(data)
        if not test_utils.in_ci():
            dump_bin_file(os.path.join(self.case_dir, f'cpu_result_{i}.bin'), data)
            dump_txt_file(os.path.join(self.case_dir, f'cpu_result_{i}.txt'), data)
        i += 1
    return results