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CODE_OF_CONDUCT.md
Violations Violations of the Code of Conduct can be reported to TensorFlow’s Project Stewards, Thea Lamkin (******@****.***) and Joana Carrasqueira (******@****.***). The Project Steward will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution...
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation build --distinct_host_configuration=false # Disable clang extension that rejects type definitions within offsetof. # This was added in clang-16 by https://reviews.llvm.org/D133574. # Can be removed once upb is updated, since a type definition is used within # offset of in the current version of ubp.
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tensorflow/c/c_api.cc
mutex_lock session_lock(it.first->mu); if (it.first->last_num_graph_nodes > op.node.id()) { it.second = strings::StrCat( "Operation '", op.node.DebugString(), "' was changed by ", mutation_type, " after it was run by a session. This mutation will have no effect, " "and will trigger an error in the future. Either don't modify " "nodes after running them or create a new session.");
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tensorflow/c/experimental/filesystem/plugins/gcs/expiring_lru_cache.h
// `key`, false if the entry was not found. In both cases, there is no entry // with key `key` existed after the call. bool Delete(const std::string& key) { absl::MutexLock lock(&mu_); return DeleteLocked(key); } /// Look up the entry with key `key` and copy it to `value` if found. Returns /// true if an entry was found for `key`, and its timestamp is not more than
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ci/official/containers/linux_arm64/builder.devtoolset/build_devtoolset.sh
mkdir -p ${TARGET}/usr/include # Put the current kernel headers from ubuntu in place. ln -s "/usr/include/linux" "${TARGET}/usr/include/linux" ln -s "/usr/include/asm-generic" "${TARGET}/usr/include/asm-generic" ln -s "/usr/include/aarch64-linux-gnu/asm" "${TARGET}/usr/include/asm" # Download glibc's shared and development libraries based on the value of the # `VERSION` parameter.
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ci/official/containers/linux_arm64/devel.usertools/rename_and_verify_wheels.sh
for wheel in /tf/pkg/*.whl; do echo "Checking and renaming $wheel..." time python3 -m auditwheel repair --plat manylinux2014_aarch64 "$wheel" --wheel-dir /tf/pkg 2>&1 | tee check.txt # We don't need the original wheel if it was renamed new_wheel=$(grep --extended-regexp --only-matching '/tf/pkg/\S+.whl' check.txt) if [[ "$new_wheel" != "$wheel" ]]; then rm "$wheel" wheel="$new_wheel" fi
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tensorflow/c/eager/c_api_experimental.h
// or construct a transformed version, by inspecting the passed `op`. // // TFE_OpGetDevice(op) records the original placement of the operation. It may // be an empty string if no device was explicitly requested, but will // otherwise be the name of this custom device. Ops are placed onto a custom // device if any of their inputs are on that custom device, but custom devices
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.h
/// been a full block). /// 3) `TF_OUT_OF_RANGE` if the read from the remote filesystem succeeded, but /// the file contents do not extend past `offset` and thus nothing was /// placed in `out`. /// 4) `TF_OK` otherwise (i.e. the read succeeded, and at least one byte was /// placed /// in `buffer`). /// /// Caller is responsible for allocating memory for `buffer`. /// `buffer` will be left unchanged in case of errors.
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tensorflow/c/experimental/filesystem/filesystem_interface.h
/// Creates a new random access read-only file from given `path`. /// /// After this call `file` may be concurrently accessed by multiple threads. /// /// Plugins: /// * Must set `status` to `TF_OK` if `file` was updated. /// * Must set `status` to `TF_NOT_FOUND` if `path` doesn't point to an /// existing file or one of the parent entries in `path` doesn't exist.
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tensorflow/c/eager/gradients.h
// // `GetID` returns a unique id for the wrapped tensor which is used to maintain // a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of // the op that produced it (or -1 if this tensor was watched using // `GradientTape::Watch`.) The op_id is simply a unique index assigned to each // op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
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