variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for

    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [python3.9, python3.10, python3.11, python3.12]
    permissions:
      contents: read
      pull-requests: write
    steps:
      - name: Delete unnecessary tools folder
        run: |
          df -h
          rm -rf /opt/hostedtoolcache
          df -h
      -
        name: Checkout

*   Fixes an integer overflow in TFLite
    ([CVE-2022-23559](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23559))
*   Fixes a dangerous OOB write in TFLite
    ([CVE-2022-23561](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23561))
*   Fixes a vulnerability leading to read and write outside of bounds in TFLite
    ([CVE-2022-23560](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23560))

  void (*cleanup)(TF_WritableFile* file);

  /// Appends `buffer` of size `n` to `*file`.
  ///
  /// Core TensorFlow owns `buffer` and guarantees at least `n` bytes of storage
  /// that can be used to write data.
  ///
  /// Note: the `buffer` argument is NOT a null terminated string!
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if exactly `n` bytes have been written.

    return;
  }
  TF_VLog(3, "Append: gs://%s/%s size %u", gcs_file->bucket.c_str(),
          gcs_file->object.c_str(), n);
  gcs_file->sync_need = true;
  gcs_file->outfile.write(buffer, n);
  if (!gcs_file->outfile)
    TF_SetStatus(status, TF_INTERNAL,
                 "Could not append to the internal temporary file.");
  else
    TF_SetStatus(status, TF_OK, "");
}

    srcs = [":tf_custom_op_library_additional_deps.dll"],
    output_group = "def_file",
)

# Filter the DEF file to reduce the number of symbols to 64K or less.
# Note that we also write the name of the pyd file into DEF file so that
# the dynamic libraries of custom ops can find it at runtime.
genrule(
    name = "tensorflow_filtered_def_file",
    srcs = [":tensorflow_def_file"],

    if e.errno == errno.EEXIST:
      os.remove(link_name)
      os.symlink(target, link_name)
    else:
      raise e


def write_to_bazelrc(line):
  with open(_TF_BAZELRC, 'a') as f:
    f.write(line + '\n')


def write_action_env_to_bazelrc(var_name, var):
  write_to_bazelrc('build --action_env {}="{}"'.format(var_name, str(var)))


def run_shell(cmd, allow_non_zero=False, stderr=None):

permissions:
  contents: read

jobs:
  close-issues:
    # Don't do this in forks
    if: github.repository == 'tensorflow/tensorflow'
    runs-on: ubuntu-latest
    permissions:
      issues: write
      pull-requests: write
    steps:
      - name: Awaiting response issues
        uses: actions/stale@6f05e4244c9a0b2ed3401882b05d701dd0a7289b # v7.0.0
        with:

// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.
// TODO: Migrate to TF_CreateConfig() below.
TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options,
                                                   unsigned char enable);

  # e.g. `declare -x TFCI_BAZEL_COMMON_ARGS="list of args"` for bash.
  export -p | grep TFCI > "$FROM_ENV"

  # Source the default ci values
  source ./ci/official/envs/ci_default

  # TODO(angerson) write this documentation
  # Sources every env, in order, from the comma-separated list "TFCI"
  # Assumes variables will resolve themselves correctly.
  set +u
  for env_file in ${TFCI//,/ }; do