일단 커밋. 오랫동안 커밋을 안해서 꼬였다.

리팩토리 중.

.venv/lib/python3.12/site-packages/cloudpickle-3.0.0.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: cloudpickle
+Version: 3.0.0
+Summary: Pickler class to extend the standard pickle.Pickler functionality
+Home-page: https://github.com/cloudpipe/cloudpickle
+License: BSD-3-Clause
+Author: The cloudpickle developer team
+Author-email: cloudpipe@googlegroups.com
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: System :: Distributed Computing
+
+# cloudpickle
+
+[![Automated Tests](https://github.com/cloudpipe/cloudpickle/workflows/Automated%20Tests/badge.svg?branch=master&event=push)](https://github.com/cloudpipe/cloudpickle/actions)
+[![codecov.io](https://codecov.io/github/cloudpipe/cloudpickle/coverage.svg?branch=master)](https://codecov.io/github/cloudpipe/cloudpickle?branch=master)
+
+`cloudpickle` makes it possible to serialize Python constructs not supported
+by the default `pickle` module from the Python standard library.
+
+`cloudpickle` is especially useful for **cluster computing** where Python
+code is shipped over the network to execute on remote hosts, possibly close
+to the data.
+
+Among other things, `cloudpickle` supports pickling for **lambda functions**
+along with **functions and classes defined interactively** in the
+`__main__` module (for instance in a script, a shell or a Jupyter notebook).
+
+Cloudpickle can only be used to send objects between the **exact same version
+of Python**.
+
+Using `cloudpickle` for **long-term object storage is not supported and
+strongly discouraged.**
+
+**Security notice**: one should **only load pickle data from trusted sources** as
+otherwise `pickle.load` can lead to arbitrary code execution resulting in a critical
+security vulnerability.
+
+
+Installation
+------------
+
+The latest release of `cloudpickle` is available from
+[pypi](https://pypi.python.org/pypi/cloudpickle):
+
+    pip install cloudpickle
+
+
+Examples
+--------
+
+Pickling a lambda expression:
+
+```python
+>>> import cloudpickle
+>>> squared = lambda x: x ** 2
+>>> pickled_lambda = cloudpickle.dumps(squared)
+
+>>> import pickle
+>>> new_squared = pickle.loads(pickled_lambda)
+>>> new_squared(2)
+4
+```
+
+Pickling a function interactively defined in a Python shell session
+(in the `__main__` module):
+
+```python
+>>> CONSTANT = 42
+>>> def my_function(data: int) -> int:
+...     return data + CONSTANT
+...
+>>> pickled_function = cloudpickle.dumps(my_function)
+>>> depickled_function = pickle.loads(pickled_function)
+>>> depickled_function
+<function __main__.my_function(data:int) -> int>
+>>> depickled_function(43)
+85
+```
+
+
+Overriding pickle's serialization mechanism for importable constructs:
+----------------------------------------------------------------------
+
+An important difference between `cloudpickle` and `pickle` is that
+`cloudpickle` can serialize a function or class **by value**, whereas `pickle`
+can only serialize it **by reference**. Serialization by reference treats
+functions and classes as attributes of modules, and pickles them through
+instructions that trigger the import of their module at load time.
+Serialization by reference is thus limited in that it assumes that the module
+containing the function or class is available/importable in the unpickling
+environment. This assumption breaks when pickling constructs defined in an
+interactive session, a case that is automatically detected by `cloudpickle`,
+that pickles such constructs **by value**.
+
+Another case where the importability assumption is expected to break is when
+developing a module in a distributed execution environment: the worker
+processes may not have access to the said module, for example if they live on a
+different machine than the process in which the module is being developed. By
+itself, `cloudpickle` cannot detect such "locally importable" modules and
+switch to serialization by value; instead, it relies on its default mode, which
+is serialization by reference. However, since `cloudpickle 2.0.0`, one can
+explicitly specify modules for which serialization by value should be used,
+using the
+`register_pickle_by_value(module)`/`/unregister_pickle_by_value(module)` API:
+
+```python
+>>> import cloudpickle
+>>> import my_module
+>>> cloudpickle.register_pickle_by_value(my_module)
+>>> cloudpickle.dumps(my_module.my_function)  # my_function is pickled by value
+>>> cloudpickle.unregister_pickle_by_value(my_module)
+>>> cloudpickle.dumps(my_module.my_function)  # my_function is pickled by reference
+```
+
+Using this API, there is no need to re-install the new version of the module on
+all the worker nodes nor to restart the workers: restarting the client Python
+process with the new source code is enough.
+
+Note that this feature is still **experimental**, and may fail in the following
+situations:
+
+- If the body of a function/class pickled by value contains an `import` statement:
+  ```python
+  >>> def f():
+  >>> ... from another_module import g
+  >>> ... # calling f in the unpickling environment may fail if another_module
+  >>> ... # is unavailable
+  >>> ... return g() + 1
+  ```
+
+- If a function pickled by reference uses a function pickled by value during its execution.
+
+
+Running the tests
+-----------------
+
+- With `tox`, to test run the tests for all the supported versions of
+  Python and PyPy:
+
+      pip install tox
+      tox
+
+  or alternatively for a specific environment:
+
+      tox -e py312
+
+
+- With `pytest` to only run the tests for your current version of
+  Python:
+
+      pip install -r dev-requirements.txt
+      PYTHONPATH='.:tests' pytest
+
+History
+-------
+
+`cloudpickle` was initially developed by [picloud.com](http://web.archive.org/web/20140721022102/http://blog.picloud.com/2013/11/17/picloud-has-joined-dropbox/) and shipped as part of
+the client SDK.
+
+A copy of `cloudpickle.py` was included as part of PySpark, the Python
+interface to [Apache Spark](https://spark.apache.org/). Davies Liu, Josh
+Rosen, Thom Neale and other Apache Spark developers improved it significantly,
+most notably to add support for PyPy and Python 3.
+
+The aim of the `cloudpickle` project is to make that work available to a wider
+audience outside of the Spark ecosystem and to make it easier to improve it
+further notably with the help of a dedicated non-regression test suite.
+