From 170cd45bb654980a654bcfeda5e795d03b66cb71 Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Sun, 9 Jun 2024 08:32:53 +0800
Subject: [PATCH] 0.1.109

---
 PyPI/setup.cfg                    |  2 +-
 PyPI/src/guan.egg-info/PKG-INFO   |  2 +-
 PyPI/src/guan/machine_learning.py | 12 ++++++++++++
 3 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index 556ff54..f2483d3 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.108
+version = 0.1.109
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO
index 6f8b933..aafa889 100644
--- a/PyPI/src/guan.egg-info/PKG-INFO
+++ b/PyPI/src/guan.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.108
+Version: 0.1.109
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/PyPI/src/guan/machine_learning.py b/PyPI/src/guan/machine_learning.py
index 455f084..b70c380 100644
--- a/PyPI/src/guan/machine_learning.py
+++ b/PyPI/src/guan/machine_learning.py
@@ -254,6 +254,12 @@ def save_model(model, filename='./model.pth'):
     import torch
     torch.save(model, filename)
 
+# 使用TorchScript形式保存模型到文件（TorchScript允许模型在不依赖Python解释器的情况下运行，适合在生产环境中部署）
+def save_model_with_torch_jit_script(model, filename='model_scripted_with_torch_jit.pth'):
+    import torch
+    scripted_model = torch.jit.script(model)
+    scripted_model.save(filename)
+
 # 以字典的形式保存模型的所有信息到文件（保存时需要模型的类可访问，此外还要输入模型的实例化函数）
 def save_model_with_all_information(model, model_class, model_instantiation, note='', filename='./model_with_all_information.pth'):
     import torch
@@ -281,6 +287,12 @@ def load_model(filename='./model.pth'):
     model = torch.load(filename)
     return model
 
+# 加载TorchScript形式的模型
+def load_model_with_torch_jit_script(filename='model_scripted_with_torch_jit.pth'):
+    import torch
+    scripted_model = torch.jit.load(filename)
+    return scripted_model
+
 # 加载包含所有信息的模型（包含了模型的类和实例化函数等，返回的是模型对象）
 def load_model_with_all_information(filename='./model_with_all_information.pth', note_print=0):
     import torch