automatic import of python-torch-geometric

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diff --git a/python-torch-geometric.spec b/python-torch-geometric.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-torch-geometric
+Version:	2.3.0
+Release:	1
+Summary:	Graph Neural Network Library for PyTorch
+License:	MIT License
+URL:		https://pypi.org/project/torch-geometric/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/43/b5/be9795db7756e6c1fa2606c8145ec637552487e72c6428ed0b231f8bcbd3/torch_geometric-2.3.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+[![PyPI Version][pypi-image]][pypi-url]
+[![Testing Status][testing-image]][testing-url]
+[![Linting Status][linting-image]][linting-url]
+[![Docs Status][docs-image]][docs-url]
+[![Contributing][contributing-image]][contributing-url]
+[![Slack][slack-image]][slack-url]
+**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[Colab Notebooks and Video Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** | **[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**
+**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
+It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
+In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/advanced/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
+**[Click here to join our Slack community!][slack-url]**
+<p align="center">
+  <a href="https://medium.com/stanford-cs224w"><img style="max-width=: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a>
+
+%package -n python3-torch-geometric
+Summary:	Graph Neural Network Library for PyTorch
+Provides:	python-torch-geometric
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-torch-geometric
+[![PyPI Version][pypi-image]][pypi-url]
+[![Testing Status][testing-image]][testing-url]
+[![Linting Status][linting-image]][linting-url]
+[![Docs Status][docs-image]][docs-url]
+[![Contributing][contributing-image]][contributing-url]
+[![Slack][slack-image]][slack-url]
+**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[Colab Notebooks and Video Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** | **[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**
+**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
+It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
+In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/advanced/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
+**[Click here to join our Slack community!][slack-url]**
+<p align="center">
+  <a href="https://medium.com/stanford-cs224w"><img style="max-width=: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a>
+
+%package help
+Summary:	Development documents and examples for torch-geometric
+Provides:	python3-torch-geometric-doc
+%description help
+[![PyPI Version][pypi-image]][pypi-url]
+[![Testing Status][testing-image]][testing-url]
+[![Linting Status][linting-image]][linting-url]
+[![Docs Status][docs-image]][docs-url]
+[![Contributing][contributing-image]][contributing-url]
+[![Slack][slack-image]][slack-url]
+**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[Colab Notebooks and Video Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** | **[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**
+**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
+It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
+In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/advanced/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
+**[Click here to join our Slack community!][slack-url]**
+<p align="center">
+  <a href="https://medium.com/stanford-cs224w"><img style="max-width=: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a>
+
+%prep
+%autosetup -n torch-geometric-2.3.0
+
+%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-torch-geometric -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2.3.0-1
+- Package Spec generated