// Note that `matches` is allocated by the plugin and freed by core
  // TensorFlow, so we need to use `plugin_memory_free_` here.
  char** matches = nullptr;
  const int num_matches = ops_->get_matching_paths(
      filesystem_.get(), pattern.c_str(), &matches, plugin_status.get());
  if (num_matches >= 0) {
    for (int i = 0; i < num_matches; i++) {
      result->push_back(std::string(matches[i]));

# nightly job. update_version.py affects TF_VER_SUFFIX, TF_VER_PYTHON, and
# TF_VER_FULL.

# Note: in awk, the command '/search/ {commands}' applies the commands to any line that
# matches the /search/ regular expression. "print $N" prints the Nth "field",
# where fields are strings separated by whitespace.
export TF_VER_MAJOR=$(awk '/#define TF_MAJOR_VERSION/ {print $3}' tensorflow/core/public/version.h)

  rpm2cpio "devtoolset-10-gcc-10.2.1-11.2.el7.src.rpm" |cpio -idmv
  tar -xvf "gcc-10.2.1-20210130.tar.xz" --strip 1
  ;;
esac

# Apply the devtoolset patches to gcc.
/rpm-patch.sh "gcc.spec"

./contrib/download_prerequisites

mkdir -p "${TARGET}-build"
cd "${TARGET}-build"

"${TARGET}-src/configure" \
      --prefix="${TARGET}/usr" \

// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;

![image](https://github.com/tensorflow/tensorflow/assets/52792999/3eea4ca5-daa0-4570-b0b5-2a2b03a724a3)

### Contributor License Agreements

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

  // Adds this tensor to the list of watched tensors.
  //
  // This is a no-op if the tensor is already being watched either from an
  // earlier call to `GradientTape::Watch` or being an output of an op with
  // watched inputs.
  void Watch(const AbstractTensorHandle*);
  // Records an operation with given inputs and outputs
  // on the tape and marks all its outputs as watched if at

dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

  /// pattern: { term }
  /// term:
  ///   '*': matches any sequence of non-'/' characters
  ///   '?': matches a single non-'/' character
  ///   '[' [ '^' ] { match-list } ']':
  ///        matches any single character (not) on the list
  ///   c: matches character c (c != '*', '?', '\\', '[')
  ///   '\\' c: matches character c
  /// character-range:
  ///   c: matches character c (c != '\\', '-', ']')

*   `tf.distribute.experimental.MultiWorkerMirroredStrategy` adds support for
    partial batches. Workers running out of data now continue to participate in
    the training with empty inputs, instead of raising an error. Learn more
    about
    [partial batches here](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).
*   Improve the performance of reading metrics eagerly under