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CONTRIBUTING.md
* [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1) * [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1) * [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)
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.github/workflows/update-nightly.yml
name: Set nightly branch to master HEAD permissions: {} jobs: master-to-nightly: if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks runs-on: ubuntu-latest permissions: contents: write steps: - uses: zofrex/mirror-branch@a8809f0b42f9dfe9b2c5c2162a46327c23d15266 # v1.0.3 name: Set nightly branch to master HEAD with:
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ci/official/utilities/cleanup_summary.sh
function resultstore_extract_fallback { # In case the main script fails somehow. cat <<EOF IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you can view more detailed results that are probably easier to read than this log. Try the links below: EOF # Find any "Streaming build results to" line, then print the last word in it, # and don't print duplicates
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.github/workflows/release-branch-cherrypick.yml
# Usage: Go to # https://github.com/tensorflow/tensorflow/actions/workflows/release-branch-cherrypick.yml # and click "Run Workflow." Leave "Use Workflow From" set to "master", then # input the branch name and paste the cherry-pick commit and click Run. A PR # will be created. name: Release Branch Cherrypick on: workflow_dispatch: inputs:
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ci/official/README.md
# Ex. disk_cache -- Use a local cache # Ex. public_cache -- Use TF's public cache (read-only) # Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only) # Ex. rbe -- Use RBE for faster builds (Googlers only; see below) # Ex. no_docker -- Disable docker on enabled platforms # See full examples below for more details on these. Some other modifiers are:
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tensorflow/api_template_v1.__init__.py
_CONTRIB_WARNING = """ The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md * https://github.com/tensorflow/addons * https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue. """
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RELEASE.md
[`tf.debugging`](https://www.tensorflow.org/versions/master/api_docs/python/tf/debugging), [`tf.dtypes`](https://www.tensorflow.org/versions/master/api_docs/python/tf/dtypes), [`tf.image`](https://www.tensorflow.org/versions/master/api_docs/python/tf/image), [`tf.io`](https://www.tensorflow.org/versions/master/api_docs/python/tf/io), [`tf.linalg`](https://www.tensorflow.org/versions/master/api_docs/python/tf/linalg),
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SECURITY.md
Eager mode lets users write imperative-style statements that can be easily inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed.
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tensorflow/c/eager/c_api_experimental.h
// creating a new op every time. If `raw_device_name` is `NULL` or empty, it // does not set the device name. If it's not `NULL`, then it attempts to parse // and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster // than separately calling it because if the existing op has the same // `raw_device_name`, it skips parsing and just leave as it is. TF_CAPI_EXPORT extern void TFE_OpReset(TFE_Op* op_to_reset,
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LICENSE
comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License");
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