def METHOD_NAME(self, output_name: str, output_data: np.ndarray, full_message: bool):
    output_name = output_name.replace("/", "_")
    output_name = output_name.replace(":", "_")
    output_path = os.path.join(self.dump_dir_path, output_name + ".txt")
    with open(output_path, "w") as f:
        if full_message == True:
            # number of output
            f.write("1\n")
            output_shape = output_data.shape
            data_type = 0
            if output_data.dtype == np.int64 or output_data.dtype == np.int32:
                data_type = 3
            elif output_data.dtype == np.int8 or output_data.dtype == np.bool:
                data_type = 2
            description = "{} {} ".format(output_name, len(output_shape))
            for dim in output_shape:
                description += "{} ".format(dim)
            description += "{}".format(str(data_type))
            f.write(description + "\n")
        # keep the same as run_onnx in align_model.py
        if output_data.dtype == np.int64 or output_data.dtype == np.int32:
            np.savetxt(f, output_data.reshape(-1), fmt="%d")
        elif output_data.dtype == np.int8 or output_data.dtype == np.bool:
            np.savetxt(f, output_data.reshape(-1), fmt="%d")
        elif output_data.dtype == np.float32 or output_data.dtype == np.float64:
            np.savetxt(f, output_data.reshape(-1), fmt="%0.6f")
        else :
            print("ERROR: dump_single_output dont support data type: " + str(output_data.dtype))
            return False
    return True