automatic import of python-metann

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+%global _empty_manifest_terminate_build 0
+Name:		python-MetaNN
+Version:	0.3.2
+Release:	1
+Summary:	MetaNN provides extensions of PyTorch nn.Module for meta learning
+License:	MIT License
+URL:		https://github.com/yhqjohn/MetaModule
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6c/03/f8b81237ad7930ed19b3b7e4cdc5aa5ef4c60cc1936cdb102558d993bcb5/MetaNN-0.3.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+
+%description
+1. Introduction
+____________________
+In meta learner scenario, it is common use dependent variables as parameters, and back propagate the gradient of the parameters. However, parameters of PyTorch Module are designed to be leaf nodes and it is forbidden for parameters to have grad_fn. Meta learning coders are therefore forced to rewrite the basic layers to adapt the meta learning requirements.
+This module provide an extension of torch.nn.Module, DependentModule that has dependent parameters, allowing the differentiable dependent parameters. It also provide the method to transform nn.Module into DependentModule, and turning all of the parameters of a nn.Module into dependent parameters.
+2. Installation
+__________________
+    pip install MetaNN
+3. Example
+___________
+PyTorch suggest all parameters of a module to be independent variables. Using DependentModule arbitrary torch.nn.module can be transformed into dependent module.
+    from metann import DependentModule
+    from torch import nn
+    net = torch.nn.Sequential(
+        nn.Linear(10, 100),
+        nn.Linear(100, 5))
+    net = DependentModule(net)
+    print(net)
+Higher-level api such as MAML class are more recommended to use.
+    from metann.meta import MAML, default_evaluator_classification as evaluator
+    from torch import nn
+    net = torch.nn.Sequential(
+        nn.Linear(10, 100),
+        nn.Linear(100, 5))
+    )
+    maml = MAML(net, steps_train=5, steps_eval=10, lr=0.01)
+    output = maml(data_train)
+    loss = evaluator(output, data_test)
+    loss.backward()
+4. Documents
+_____________
+The documents are available at ReadTheDocs.
+`MetaNN <https://metann.readthedocs.io/>`__
+5. License
+__________
+`MIT <http://opensource.org/licenses/MIT>`__
+Copyright (c) 2019-present, Hanqiao Yu
+
+%package -n python3-MetaNN
+Summary:	MetaNN provides extensions of PyTorch nn.Module for meta learning
+Provides:	python-MetaNN
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-MetaNN
+1. Introduction
+____________________
+In meta learner scenario, it is common use dependent variables as parameters, and back propagate the gradient of the parameters. However, parameters of PyTorch Module are designed to be leaf nodes and it is forbidden for parameters to have grad_fn. Meta learning coders are therefore forced to rewrite the basic layers to adapt the meta learning requirements.
+This module provide an extension of torch.nn.Module, DependentModule that has dependent parameters, allowing the differentiable dependent parameters. It also provide the method to transform nn.Module into DependentModule, and turning all of the parameters of a nn.Module into dependent parameters.
+2. Installation
+__________________
+    pip install MetaNN
+3. Example
+___________
+PyTorch suggest all parameters of a module to be independent variables. Using DependentModule arbitrary torch.nn.module can be transformed into dependent module.
+    from metann import DependentModule
+    from torch import nn
+    net = torch.nn.Sequential(
+        nn.Linear(10, 100),
+        nn.Linear(100, 5))
+    net = DependentModule(net)
+    print(net)
+Higher-level api such as MAML class are more recommended to use.
+    from metann.meta import MAML, default_evaluator_classification as evaluator
+    from torch import nn
+    net = torch.nn.Sequential(
+        nn.Linear(10, 100),
+        nn.Linear(100, 5))
+    )
+    maml = MAML(net, steps_train=5, steps_eval=10, lr=0.01)
+    output = maml(data_train)
+    loss = evaluator(output, data_test)
+    loss.backward()
+4. Documents
+_____________
+The documents are available at ReadTheDocs.
+`MetaNN <https://metann.readthedocs.io/>`__
+5. License
+__________
+`MIT <http://opensource.org/licenses/MIT>`__
+Copyright (c) 2019-present, Hanqiao Yu
+
+%package help
+Summary:	Development documents and examples for MetaNN
+Provides:	python3-MetaNN-doc
+%description help
+1. Introduction
+____________________
+In meta learner scenario, it is common use dependent variables as parameters, and back propagate the gradient of the parameters. However, parameters of PyTorch Module are designed to be leaf nodes and it is forbidden for parameters to have grad_fn. Meta learning coders are therefore forced to rewrite the basic layers to adapt the meta learning requirements.
+This module provide an extension of torch.nn.Module, DependentModule that has dependent parameters, allowing the differentiable dependent parameters. It also provide the method to transform nn.Module into DependentModule, and turning all of the parameters of a nn.Module into dependent parameters.
+2. Installation
+__________________
+    pip install MetaNN
+3. Example
+___________
+PyTorch suggest all parameters of a module to be independent variables. Using DependentModule arbitrary torch.nn.module can be transformed into dependent module.
+    from metann import DependentModule
+    from torch import nn
+    net = torch.nn.Sequential(
+        nn.Linear(10, 100),
+        nn.Linear(100, 5))
+    net = DependentModule(net)
+    print(net)
+Higher-level api such as MAML class are more recommended to use.
+    from metann.meta import MAML, default_evaluator_classification as evaluator
+    from torch import nn
+    net = torch.nn.Sequential(
+        nn.Linear(10, 100),
+        nn.Linear(100, 5))
+    )
+    maml = MAML(net, steps_train=5, steps_eval=10, lr=0.01)
+    output = maml(data_train)
+    loss = evaluator(output, data_test)
+    loss.backward()
+4. Documents
+_____________
+The documents are available at ReadTheDocs.
+`MetaNN <https://metann.readthedocs.io/>`__
+5. License
+__________
+`MIT <http://opensource.org/licenses/MIT>`__
+Copyright (c) 2019-present, Hanqiao Yu
+
+%prep
+%autosetup -n MetaNN-0.3.2
+
+%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-MetaNN -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.2-1
+- Package Spec generated