one question, how to do Incremental learning in drain3 training?

[CH-nolyn]

`import json
import logging
import sys
import time
from util.config_reader import initialize_template_config
from util.httpserver_operation import training_post_model
from drain3.file_persistence import FilePersistence
from drain3 import TemplateMiner

def process_log_training(raw_log_path, query_data):
logger = logging.getLogger(name)
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(message)s')
scenario = query_data["scenario"]
output_file = f"{scenario}/drain3_state.bin"
persistence = FilePersistence(output_file)

template_miner = TemplateMiner(persistence, config=initialize_template_config(profiling_enabled=True))

line_count = 0
with open(raw_log_path, encoding='utf-8') as f:
    lines = f.readlines()

start_time = time.time()
batch_start_time = start_time
batch_size = 10000
# 逐行训练
for line in lines:
    line = line.rstrip()
    result = template_miner.add_log_message(line)
    line_count += 1
    if line_count % batch_size == 0:
        time_took = time.time() - batch_start_time
        rate = batch_size / time_took
        logger.info(f"Processing line: {line_count}, rate {rate:.1f} lines/sec, "
                    f"{len(template_miner.drain.clusters)} clusters so far.")
        batch_start_time = time.time()
    if result["change_type"] != "none":
        result_json = json.dumps({
            result["cluster_id"]: {
                "template_mined": result["template_mined"]
            }
        })
        logger.info(f"Input ({line_count}): " + line)
        logger.info("Result: " + result_json)

time_took = time.time() - start_time
rate = line_count / time_took
logger.info(
    f"--- Done processing file in {time_took:.2f} sec. Total of {line_count} lines, rate {rate:.1f} lines/sec, "
    f"{len(template_miner.drain.clusters)} clusters")

sorted_clusters = sorted(template_miner.drain.clusters, key=lambda it: it.size, reverse=True)
for cluster in sorted_clusters:
    logger.info(cluster)

print("Prefix Tree:")
template_miner.drain.print_tree()
template_miner.profiler.report(0)

training_post_model(output_file)

`
this is my training code ,so how could i train new log by ex-trained model.