METADATA published to PyPI require tensorflow<2.16.0

[DavidPal]

The metadata of tensorflow-ranking package require tensorflow <2.16.0

The METADATA published to PyPI as part of the package it impossible to install tensorflow-ranking package alongside tensorflow==2.16.X or tensorflow==2.17.X or future versions of tensorflow==2.XX.YY.

Commands that I ran on my Macbook to get METADATA:

pip install tensorflow-ranking==0.5.5

cat ~/.pyenv/versions/tensorflow-notebook/lib/python3.9/site-packages/tensorflow_ranking-0.5.5.dist-info/METADATA

Here is the METADATA

Metadata-Version: 2.1
Name: tensorflow-ranking
Version: 0.5.5
Summary: Pip package setup file for TensorFlow Ranking.
Home-page: https://github.com/tensorflow/ranking
Author: Google Inc.
Author-email: [email protected]
License: Apache 2.0
Keywords: tensorflow ranking learning-to-rank
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: absl-py >=0.1.6
Requires-Dist: numpy >=1.23.2
Requires-Dist: six >=1.10.0
Requires-Dist: tensorflow-serving-api <3.0.0,>=2.0.0
Requires-Dist: tensorflow <2.16.0

TensorFlow Ranking is a framework to define learning-to-rank models.

When using poetry, it is impossible to install tensorflow==2.17.0 and tensorflow-ranking==0.5.5 at the same time.