automatic import of python-neural-structured-learning

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+/neural-structured-learning-1.4.0.tar.gz
diff --git a/python-neural-structured-learning.spec b/python-neural-structured-learning.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-neural-structured-learning
+Version:	1.4.0
+Release:	1
+Summary:	Neural Structured Learning is an open-source TensorFlow framework to train neural networks with structured signals
+License:	Apache Software License
+URL:		https://github.com/tensorflow/neural-structured-learning
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ca/ee/532c37293c61624f8e6e3db05bb232259479027ba2b5c6d5b4d2f327db25/neural-structured-learning-1.4.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-absl-py
+Requires:	python3-attrs
+Requires:	python3-scipy
+Requires:	python3-six
+
+%description
+Neural Structured Learning (NSL) is a new learning paradigm to train neural
+networks by leveraging structured signals in addition to feature inputs.
+Structure can be explicit as represented by a graph or implicit as induced
+by adversarial perturbation.
+
+Structured signals are commonly used to represent relations or similarity
+among samples that may be labeled or unlabeled. Leveraging these signals
+during neural network training harnesses both labeled and unlabeled data,
+which can improve model accuracy, particularly when the amount of labeled
+data is relatively small. Additionally, models trained with samples that are
+generated by adversarial perturbation have been shown to be robust against
+malicious attacks, which are designed to mislead a model's prediction or
+classification.
+
+NSL generalizes to Neural Graph Learning as well as to Adversarial Learning.
+The NSL framework in TensorFlow provides the following easy-to-use APIs and
+tools for developers to train models with structured signals:
+
+* Keras APIs to enable training with graphs (explicit structure) and
+  adversarial perturbations (implicit structure).
+
+* TF ops and functions to enable training with structure when using
+  lower-level TensorFlow APIs.
+
+* Tools to build graphs and construct graph inputs for training.
+
+The NSL framework is designed to be flexible and can be used to train any
+kind of neural network. For example, feed-forward, convolution, and
+recurrent neural networks can all be trained using the NSL framework. In
+addition to supervised and semi-supervised learning (a low amount of
+supervision), NSL can in theory be generalized to unsupervised learning.
+Incorporating structured signals is done only during training, so the
+performance of the serving/inference workflow remains unchanged.
+
+
+
+%package -n python3-neural-structured-learning
+Summary:	Neural Structured Learning is an open-source TensorFlow framework to train neural networks with structured signals
+Provides:	python-neural-structured-learning
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-neural-structured-learning
+Neural Structured Learning (NSL) is a new learning paradigm to train neural
+networks by leveraging structured signals in addition to feature inputs.
+Structure can be explicit as represented by a graph or implicit as induced
+by adversarial perturbation.
+
+Structured signals are commonly used to represent relations or similarity
+among samples that may be labeled or unlabeled. Leveraging these signals
+during neural network training harnesses both labeled and unlabeled data,
+which can improve model accuracy, particularly when the amount of labeled
+data is relatively small. Additionally, models trained with samples that are
+generated by adversarial perturbation have been shown to be robust against
+malicious attacks, which are designed to mislead a model's prediction or
+classification.
+
+NSL generalizes to Neural Graph Learning as well as to Adversarial Learning.
+The NSL framework in TensorFlow provides the following easy-to-use APIs and
+tools for developers to train models with structured signals:
+
+* Keras APIs to enable training with graphs (explicit structure) and
+  adversarial perturbations (implicit structure).
+
+* TF ops and functions to enable training with structure when using
+  lower-level TensorFlow APIs.
+
+* Tools to build graphs and construct graph inputs for training.
+
+The NSL framework is designed to be flexible and can be used to train any
+kind of neural network. For example, feed-forward, convolution, and
+recurrent neural networks can all be trained using the NSL framework. In
+addition to supervised and semi-supervised learning (a low amount of
+supervision), NSL can in theory be generalized to unsupervised learning.
+Incorporating structured signals is done only during training, so the
+performance of the serving/inference workflow remains unchanged.
+
+
+
+%package help
+Summary:	Development documents and examples for neural-structured-learning
+Provides:	python3-neural-structured-learning-doc
+%description help
+Neural Structured Learning (NSL) is a new learning paradigm to train neural
+networks by leveraging structured signals in addition to feature inputs.
+Structure can be explicit as represented by a graph or implicit as induced
+by adversarial perturbation.
+
+Structured signals are commonly used to represent relations or similarity
+among samples that may be labeled or unlabeled. Leveraging these signals
+during neural network training harnesses both labeled and unlabeled data,
+which can improve model accuracy, particularly when the amount of labeled
+data is relatively small. Additionally, models trained with samples that are
+generated by adversarial perturbation have been shown to be robust against
+malicious attacks, which are designed to mislead a model's prediction or
+classification.
+
+NSL generalizes to Neural Graph Learning as well as to Adversarial Learning.
+The NSL framework in TensorFlow provides the following easy-to-use APIs and
+tools for developers to train models with structured signals:
+
+* Keras APIs to enable training with graphs (explicit structure) and
+  adversarial perturbations (implicit structure).
+
+* TF ops and functions to enable training with structure when using
+  lower-level TensorFlow APIs.
+
+* Tools to build graphs and construct graph inputs for training.
+
+The NSL framework is designed to be flexible and can be used to train any
+kind of neural network. For example, feed-forward, convolution, and
+recurrent neural networks can all be trained using the NSL framework. In
+addition to supervised and semi-supervised learning (a low amount of
+supervision), NSL can in theory be generalized to unsupervised learning.
+Incorporating structured signals is done only during training, so the
+performance of the serving/inference workflow remains unchanged.
+
+
+
+%prep
+%autosetup -n neural-structured-learning-1.4.0
+
+%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-neural-structured-learning -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..b200057
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+ec720e3369d5de490ffdc75a6c93be3a  neural-structured-learning-1.4.0.tar.gz