automatic import of python-tensorforce

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diff --git a/.gitignore b/.gitignore
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+/Tensorforce-0.6.5.tar.gz
diff --git a/python-tensorforce.spec b/python-tensorforce.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-Tensorforce
+Version:	0.6.5
+Release:	1
+Summary:	Tensorforce: a TensorFlow library for applied reinforcement learning
+License:	Apache 2.0
+URL:		http://github.com/tensorforce/tensorforce
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/cd/f1/2da8d2547dc1f6f3eb04f42472bc9126c50c4e2527c784b5d52e8ae0219f/Tensorforce-0.6.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-gym
+Requires:	python3-h5py
+Requires:	python3-matplotlib
+Requires:	python3-msgpack
+Requires:	python3-msgpack-numpy
+Requires:	python3-numpy
+Requires:	python3-Pillow
+Requires:	python3-tensorflow
+Requires:	python3-tqdm
+Requires:	python3-ale-py
+Requires:	python3-pygame
+Requires:	python3-opencv-python
+Requires:	python3-m2r
+Requires:	python3-recommonmark
+Requires:	python3-sphinx
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-ale-py
+Requires:	python3-gym[atari,box2d,classic_control]
+Requires:	python3-box2d
+Requires:	python3-gym-retro
+Requires:	python3-vizdoom
+Requires:	python3-gym[box2d,classic_control]
+Requires:	python3-box2d
+Requires:	python3-gym-retro
+Requires:	python3-pytest
+Requires:	python3-tensorflow-addons
+Requires:	python3-hpbandster
+Requires:	python3-vizdoom
+
+%description
+# Tensorforce: a TensorFlow library for applied reinforcement learning
+Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.
+
+Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:
+
+- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
+- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
+- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.
+
+For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).
+
+
+
+
+%package -n python3-Tensorforce
+Summary:	Tensorforce: a TensorFlow library for applied reinforcement learning
+Provides:	python-Tensorforce
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-Tensorforce
+# Tensorforce: a TensorFlow library for applied reinforcement learning
+Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.
+
+Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:
+
+- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
+- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
+- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.
+
+For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).
+
+
+
+
+%package help
+Summary:	Development documents and examples for Tensorforce
+Provides:	python3-Tensorforce-doc
+%description help
+# Tensorforce: a TensorFlow library for applied reinforcement learning
+Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.
+
+Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:
+
+- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
+- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
+- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.
+
+For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).
+
+
+
+
+%prep
+%autosetup -n Tensorforce-0.6.5
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-Tensorforce -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..511bacc
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f9b57b097919da0e4986aa4c0714d7e8  Tensorforce-0.6.5.tar.gz