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chore: consistently apply MIT OR Apache-2.0 license and clarify license on test files
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LICENSE-APACHE

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Copyright (c) 2021 Xavier Tao, Tommy van der Vorst & WONNX contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.

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README.md

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Constant folding is performed as part of shape inference, unless disabled (from the CLI pass `--no-fold-constants` to disable). This
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is done in order to support models that dynamically calculate shapes using operators such as `Shape`/`Squeeze`/`Unsqueeze` depending
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on dynamically set dimension parameters (e.g. batch size).
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## License
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Licensed under either of
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* Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
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* MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)
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at your option.
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Except for the following files:
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* `data/models`:
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* `mobilenetv2-7.onnx`: [source](https://github.com/onnx/models/blob/main/vision/classification/mobilenet/model/mobilenetv2-7.onnx), Apache-2.0 license only.
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* `squeezenet-labels.txt`: [source](https://github.com/onnx/models/blob/main/vision/classification/synset.txt), Apache-2.0 license only.
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* `data/images`:
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* `pelican.jpeg`: [source](https://en.wikipedia.org/wiki/Pelican#/media/File:Pelikan_Walvis_Bay.jpg), (C) Rui Ornelas, [CC-BY 2.0](https://creativecommons.org/licenses/by/2.0/).
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* `bald_eagle.jpeg`: [source](https://en.wikipedia.org/wiki/Bald_eagle#/media/File:Bald-Eagle-9114-cropped.jpg), (C) David R. Tribble, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
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### Contribution
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Unless you explicitly state otherwise, any contribution intentionally submitted
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for inclusion in the work by you shall be dual licensed as above, without any
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additional terms or conditions.

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wonnx-py/tests/test_onnx_backend.py

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# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: MIT OR Apache-2.0
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import itertools
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import os

wonnx-py/tests/test_specific_op.py

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# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: MIT OR Apache-2.0
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import onnx.backend.test
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pytest_plugins = ("onnx.backend.test.report",)

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