Package: wnpp
Severity: normal

The current maintainer of torch3, Cosimo Alfarano <ka...@debian.org>,
is apparently not active anymore.  Therefore, I orphan this package now.

Maintaining a package requires time and skills. Please only adopt this
package if you will have enough time and attention to work on it.

If you want to be the new maintainer, please see
https://www.debian.org/devel/wnpp/index.html#howto-o for detailed
instructions how to adopt a package properly.

Some information about this package:

Package: torch3
Binary: libtorch3c2, libtorch3-dev
Version: 3.1-2.1
Maintainer: Cosimo Alfarano <ka...@debian.org>
Build-Depends: debhelper (>> 4.0.0)
Architecture: any
Standards-Version: 3.8.0
Format: 1.0
Files:
 896eee773d78142bca03174c0775c4f8 956 torch3_3.1-2.1.dsc
 04f7dc72a1e875a12ddf8ac77ec2dcc7 789599 torch3_3.1.orig.tar.gz
 4f6c8e743e7a0aa9688f1e20b14797e4 10822 torch3_3.1-2.1.diff.gz
Checksums-Sha1:
 8fcc252cb48d9082ec072b86726ba03d0566e588 956 torch3_3.1-2.1.dsc
 bceae284e978e3603468ab68f510a6c10d6ec0cd 789599 torch3_3.1.orig.tar.gz
 ccebb57ad446c6cdf9f3da5474b10b61ce547b7e 10822 torch3_3.1-2.1.diff.gz
Checksums-Sha256:
 730e136f2b9e97a89a688b1f54af2663acb0cba149f2ed6853f7b9b3516e1be0 956 
torch3_3.1-2.1.dsc
 503d7f02b7d717d8eee2f6e56ce4a4badc9571e5197a2ce9c3db056e34d92e29 789599 
torch3_3.1.orig.tar.gz
 8838186336273eef297ab8dd2aeb218b3ae024dc90c1f91af4d91cffa37f9d2e 10822 
torch3_3.1-2.1.diff.gz
Directory: pool/main/t/torch3
Priority: source
Section: libs

Package: torch3
Binary: libtorch3c2, libtorch3-dev
Version: 3.1-2.1
Maintainer: Cosimo Alfarano <ka...@debian.org>
Build-Depends: debhelper (>> 4.0.0)
Architecture: any
Standards-Version: 3.8.0
Format: 1.0
Files:
 896eee773d78142bca03174c0775c4f8 956 torch3_3.1-2.1.dsc
 04f7dc72a1e875a12ddf8ac77ec2dcc7 789599 torch3_3.1.orig.tar.gz
 4f6c8e743e7a0aa9688f1e20b14797e4 10822 torch3_3.1-2.1.diff.gz
Checksums-Sha256:
 730e136f2b9e97a89a688b1f54af2663acb0cba149f2ed6853f7b9b3516e1be0 956 
torch3_3.1-2.1.dsc
 503d7f02b7d717d8eee2f6e56ce4a4badc9571e5197a2ce9c3db056e34d92e29 789599 
torch3_3.1.orig.tar.gz
 8838186336273eef297ab8dd2aeb218b3ae024dc90c1f91af4d91cffa37f9d2e 10822 
torch3_3.1-2.1.diff.gz
Directory: pool/main/t/torch3
Priority: source
Section: libs

Package: libtorch3c2
Source: torch3
Version: 3.1-2.1
Installed-Size: 680
Maintainer: Cosimo Alfarano <ka...@debian.org>
Architecture: amd64
Provides: libtorch
Depends: libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1)
Conflicts: libtorch3
Description-en: State of the art machine learning library - runtime library
 Torch is a machine-learning library, written in C++.  Its aim is to
 provide the state-of-the-art of the best algorithms for
 machine-learning.
 .
  * Many gradient-based methods, including multi-layered perceptrons,
    radial basis functions, and mixtures of experts.  Many small "modules"
    (Linear module, Tanh module, SoftMax module, ...) can be plugged
    together.
  * Support Vector Machine, for classification and regression.
  * Distribution package, includes Kmeans, Gaussian Mixture Models,
    Hidden Markov Models, and Bayes Classifier, and classes for speech
    recognition with embedded training.
  * Ensemble models such as Bagging and Adaboost.
  * Non-parametric models such as K-nearest-neighbors, Parzen Regression
    and Parzen Density Estimator.
 .
 This package is the Torch runtime library.
Description-md5: 329693d5991c8e9e703b83dc6cb3ed06
Tag: role::shared-lib
Section: libs
Priority: optional
Filename: pool/main/t/torch3/libtorch3c2_3.1-2.1_amd64.deb
Size: 234952
MD5sum: 9d376b05a996e63987eb8a0f39195295
SHA1: e7d619140b46258c36d5601ebfb0b08c77b2a0bf
SHA256: 9608489dc053bb89d02a524c710b801a591bbbab261e6da2c1cb758bb3ab6eba

Package: libtorch3-dev
Source: torch3
Version: 3.1-2.1
Installed-Size: 2912
Maintainer: Cosimo Alfarano <ka...@debian.org>
Architecture: amd64
Provides: libtorch-dev
Depends: libtorch3c2 (= 3.1-2.1)
Conflicts: libtorch-dev, libtorch1-dev
Description-en: State of the art machine learning library - development files
 Torch is a machine-learning library, written in C++.  Its aim is to
 provide the state-of-the-art of the best algorithms.
 .
  * Many gradient-based methods, including multi-layered perceptrons,
    radial basis functions, and mixtures of experts.  Many small "modules"
    (Linear module, Tanh module, SoftMax module, ...) can be plugged
    together.
  * Support Vector Machine, for classification and regression.
  * Distribution package, includes Kmeans, Gaussian Mixture Models,
    Hidden Markov Models, and Bayes Classifier, and classes for speech
    recognition with embedded training.
  * Ensemble models such as Bagging and Adaboost.
  * Non-parametric models such as K-nearest-neighbors, Parzen Regression
    and Parzen Density Estimator.
 .
 This package is the Torch development package (header files and
 static library.)
Description-md5: 97624bfafba0dbe54105f0799a38e8b7
Tag: devel::library, role::devel-lib
Section: libdevel
Priority: optional
Filename: pool/main/t/torch3/libtorch3-dev_3.1-2.1_amd64.deb
Size: 961716
MD5sum: 644c0092b84c250f3917b9025f0bab46
SHA1: f4d86b5e0deeaf9d5476c06e6ddd8721e2915168
SHA256: 09b10109c0b7c488657865c409b99d1824c3d11dc8452fa85f74073c64b0b459

Package: libtorch3c2
Source: torch3
Version: 3.1-2.1
Installed-Size: 680
Maintainer: Cosimo Alfarano <ka...@debian.org>
Architecture: amd64
Provides: libtorch
Depends: libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1)
Conflicts: libtorch3
Description-en: State of the art machine learning library - runtime library
 Torch is a machine-learning library, written in C++.  Its aim is to
 provide the state-of-the-art of the best algorithms for
 machine-learning.
 .
  * Many gradient-based methods, including multi-layered perceptrons,
    radial basis functions, and mixtures of experts.  Many small "modules"
    (Linear module, Tanh module, SoftMax module, ...) can be plugged
    together.
  * Support Vector Machine, for classification and regression.
  * Distribution package, includes Kmeans, Gaussian Mixture Models,
    Hidden Markov Models, and Bayes Classifier, and classes for speech
    recognition with embedded training.
  * Ensemble models such as Bagging and Adaboost.
  * Non-parametric models such as K-nearest-neighbors, Parzen Regression
    and Parzen Density Estimator.
 .
 This package is the Torch runtime library.
Description-md5: 329693d5991c8e9e703b83dc6cb3ed06
Tag: role::shared-lib
Section: libs
Priority: optional
Filename: pool/main/t/torch3/libtorch3c2_3.1-2.1_amd64.deb
Size: 234952
MD5sum: 9d376b05a996e63987eb8a0f39195295
SHA1: e7d619140b46258c36d5601ebfb0b08c77b2a0bf
SHA256: 9608489dc053bb89d02a524c710b801a591bbbab261e6da2c1cb758bb3ab6eba

Package: libtorch3-dev
Source: torch3
Version: 3.1-2.1
Installed-Size: 2912
Maintainer: Cosimo Alfarano <ka...@debian.org>
Architecture: amd64
Provides: libtorch-dev
Depends: libtorch3c2 (= 3.1-2.1)
Conflicts: libtorch-dev, libtorch1-dev
Description-en: State of the art machine learning library - development files
 Torch is a machine-learning library, written in C++.  Its aim is to
 provide the state-of-the-art of the best algorithms.
 .
  * Many gradient-based methods, including multi-layered perceptrons,
    radial basis functions, and mixtures of experts.  Many small "modules"
    (Linear module, Tanh module, SoftMax module, ...) can be plugged
    together.
  * Support Vector Machine, for classification and regression.
  * Distribution package, includes Kmeans, Gaussian Mixture Models,
    Hidden Markov Models, and Bayes Classifier, and classes for speech
    recognition with embedded training.
  * Ensemble models such as Bagging and Adaboost.
  * Non-parametric models such as K-nearest-neighbors, Parzen Regression
    and Parzen Density Estimator.
 .
 This package is the Torch development package (header files and
 static library.)
Description-md5: 97624bfafba0dbe54105f0799a38e8b7
Tag: devel::library, role::devel-lib
Section: libdevel
Priority: optional
Filename: pool/main/t/torch3/libtorch3-dev_3.1-2.1_amd64.deb
Size: 961716
MD5sum: 644c0092b84c250f3917b9025f0bab46
SHA1: f4d86b5e0deeaf9d5476c06e6ddd8721e2915168
SHA256: 09b10109c0b7c488657865c409b99d1824c3d11dc8452fa85f74073c64b0b459

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