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chore: relicense ./scripts
Signed-off-by: usamoi <usamoi@outlook.com>
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‎.github/workflows/check.yml‎

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RS_HEADER=$(echo "$HEADER" | awk '{ if ($0 ~ /^$/) print "//"; else print "// " $0 }')
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C_HEADER=$(echo "$HEADER" | awk '{ if ($0 ~ /^$/) print "//"; else print "// " $0 }')
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PY_HEADER=$(echo "$HEADER" | awk '{ if ($0 ~ /^$/) print "#"; else print "# " $0 }')
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RS_FILES=$(find . -type f -name "*.rs" -not -path "./target/*")
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C_FILES=$(find . -type f -name "*.c" -not -path "./target/*")
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PY_FILES=$(find . -type f -name "*.py" -not -path "./target/*")
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FLAG="0"
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fi
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done
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for p in $PY_FILES; do
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if ! head -n "$COUNT" "$p" | cmp -s - <(echo "$PY_HEADER"); then
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echo "license header mismatch in file $p"
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FLAG="1"
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fi
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done
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if [ "$FLAG" -eq "1" ]; then
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exit 1
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fi

‎scripts/LICENSE‎

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Creative Commons Legal Code
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CC0 1.0 Universal
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CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
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LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN
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ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
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INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
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REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS
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PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM
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THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED
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HEREUNDER.
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Statement of Purpose
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The laws of most jurisdictions throughout the world automatically confer
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exclusive Copyright and Related Rights (defined below) upon the creator
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and subsequent owner(s) (each and all, an "owner") of an original work of
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authorship and/or a database (each, a "Work").
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Certain owners wish to permanently relinquish those rights to a Work for
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the purpose of contributing to a commons of creative, cultural and
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scientific works ("Commons") that the public can reliably and without fear
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of later claims of infringement build upon, modify, incorporate in other
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works, reuse and redistribute as freely as possible in any form whatsoever
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and for any purposes, including without limitation commercial purposes.
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These owners may contribute to the Commons to promote the ideal of a free
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limitation commercial, advertising or promotional purposes (the
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"License"). The License shall be deemed effective as of the date CC0 was
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applied by Affirmer to the Work. Should any part of the License for any
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of the License, and in such case Affirmer hereby affirms that he or she
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will not (i) exercise any of his or her remaining Copyright and Related
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action with respect to the Work, in either case contrary to Affirmer's
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express Statement of Purpose.
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4. Limitations and Disclaimers.
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a. No trademark or patent rights held by Affirmer are waived, abandoned,
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surrendered, licensed or otherwise affected by this document.
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b. Affirmer offers the Work as-is and makes no representations or
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c. Affirmer disclaims responsibility for clearing rights of other persons
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Further, Affirmer disclaims responsibility for obtaining any necessary
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d. Affirmer understands and acknowledges that Creative Commons is not a
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‎scripts/README.md‎

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## Run External Index Precomputation Toolkit
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# External Index Precomputation Toolkit
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1. Install requirements
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- If you already have your vectors stored in `PostgreSQL` using `pgvector`, you can export them to a local file by:
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```shell
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python scripts/dump.py -n [table name] -c [column name] -d [dim] -o export.hdf5 --url postgresql://USERNAME:PASSWORD@localhost:5432/DBNAME
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python dump.py -n [table name] -c [column name] -d [dim] -o export.hdf5 --url postgresql://USERNAME:PASSWORD@localhost:5432/DBNAME
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```
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- If you don't have any data, but would like to give it a try, you can choose one of these datasets:
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```shell
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# For small dataset size from 1M to 5M
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python scripts/train.py -i [dataset file(export.hdf5)] -o [centroid filename(centroid.npy)] --lists [lists] -m [metric(l2/cos/dot)]
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python train.py -i [dataset file(export.hdf5)] -o [centroid filename(centroid.npy)] --lists [lists] -m [metric(l2/cos/dot)]
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# For large datasets size, 5M to 100M in size, use GPU and mmap chunks
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python scripts/train.py -i [dataset file(export.hdf5)] -o [centroid filename(centroid.npy)] --lists [lists] -m [metric(l2/cos/dot)] -g --mmap
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python train.py -i [dataset file(export.hdf5)] -o [centroid filename(centroid.npy)] --lists [lists] -m [metric(l2/cos/dot)] -g --mmap
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```
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`lists` is the number of centroids for clustering, and a typical value for large datasets(>5M) could range from:
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3. To insert vectors and centroids into the database, and then create an index
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```shell
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python scripts/index.py -n [table name] -i [dataset file(export.hdf5)] -c [centroid filename(centroid.npy)] -m [metric(l2/cos/dot)] -d [dim] --url postgresql://postgres:123@localhost:5432/postgres
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python index.py -n [table name] -i [dataset file(export.hdf5)] -c [centroid filename(centroid.npy)] -m [metric(l2/cos/dot)] -d [dim] --url postgresql://postgres:123@localhost:5432/postgres
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```
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4. Let's start our tour to check the benchmark result of VectorChord
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```shell
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python scripts/bench.py -n [table name] -i [dataset file(export.hdf5)] -m [metric(l2/cos/dot)] --nprob 100 --epsilon 1.0 --url postgresql://postgres:123@localhost:5432/postgres
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python bench.py -n [table name] -i [dataset file(export.hdf5)] -m [metric(l2/cos/dot)] --nprob 100 --epsilon 1.0 --url postgresql://postgres:123@localhost:5432/postgres
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```
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## License
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All files in this directory are released under the **CC0 1.0 Universal (CC0 1.0) Public Domain Dedication**.
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To the extent possible under law, TensorChord Inc. has waived all copyright and related or neighboring rights to this work.
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You may copy, modify, distribute, and perform the work, even for commercial purposes, without asking permission.
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For the full legal text, see the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/legalcode).

‎scripts/bench.py‎

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# This software is licensed under a dual license model:
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# SPDX-License-Identifier: CC0-1.0
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#
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# GNU Affero General Public License v3 (AGPLv3): You may use, modify, and
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# distribute this software under the terms of the AGPLv3.
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# This file is dedicated to the public domain under CC0 1.0.
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# To the extent possible under law, TensorChord Inc. has waived all
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# copyright and related or neighboring rights to this work.
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#
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# Elastic License v2 (ELv2): You may also use, modify, and distribute this
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# software under the Elastic License v2, which has specific restrictions.
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#
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# We welcome any commercial collaboration or support. For inquiries
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# regarding the licenses, please contact us at:
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# vectorchord-inquiry@tensorchord.ai
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# You may use this work for any purpose, without restriction.
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#
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# Copyright (c) 2025 TensorChord Inc.
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‎scripts/dump.py‎

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# This software is licensed under a dual license model:
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# SPDX-License-Identifier: CC0-1.0
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#
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# GNU Affero General Public License v3 (AGPLv3): You may use, modify, and
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# distribute this software under the terms of the AGPLv3.
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# This file is dedicated to the public domain under CC0 1.0.
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# To the extent possible under law, TensorChord Inc. has waived all
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# copyright and related or neighboring rights to this work.
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#
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# Elastic License v2 (ELv2): You may also use, modify, and distribute this
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# software under the Elastic License v2, which has specific restrictions.
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#
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# We welcome any commercial collaboration or support. For inquiries
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# regarding the licenses, please contact us at:
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# vectorchord-inquiry@tensorchord.ai
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# You may use this work for any purpose, without restriction.
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#
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# Copyright (c) 2025 TensorChord Inc.
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‎scripts/index.py‎

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# This software is licensed under a dual license model:
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# SPDX-License-Identifier: CC0-1.0
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#
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# GNU Affero General Public License v3 (AGPLv3): You may use, modify, and
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# distribute this software under the terms of the AGPLv3.
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# This file is dedicated to the public domain under CC0 1.0.
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# To the extent possible under law, TensorChord Inc. has waived all
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# copyright and related or neighboring rights to this work.
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#
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# Elastic License v2 (ELv2): You may also use, modify, and distribute this
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# software under the Elastic License v2, which has specific restrictions.
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#
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# We welcome any commercial collaboration or support. For inquiries
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# regarding the licenses, please contact us at:
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# vectorchord-inquiry@tensorchord.ai
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# You may use this work for any purpose, without restriction.
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#
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# Copyright (c) 2025 TensorChord Inc.
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‎scripts/train.py‎

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# This software is licensed under a dual license model:
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# SPDX-License-Identifier: CC0-1.0
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#
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# GNU Affero General Public License v3 (AGPLv3): You may use, modify, and
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# distribute this software under the terms of the AGPLv3.
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# This file is dedicated to the public domain under CC0 1.0.
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# To the extent possible under law, TensorChord Inc. has waived all
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# copyright and related or neighboring rights to this work.
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#
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# Elastic License v2 (ELv2): You may also use, modify, and distribute this
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# software under the Elastic License v2, which has specific restrictions.
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#
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# We welcome any commercial collaboration or support. For inquiries
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# regarding the licenses, please contact us at:
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# vectorchord-inquiry@tensorchord.ai
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# You may use this work for any purpose, without restriction.
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#
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# Copyright (c) 2025 TensorChord Inc.
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