TF_DeviceList* device_list = list.get();

  CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  const int num_devices = TF_DeviceListCount(device_list);
  LOG(INFO) << "There are " << num_devices << " devices.";
  for (int i = 0; i < num_devices; ++i) {
    const char* device_name = TF_DeviceListName(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks
      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch:
        description: 'Release branch name (e.g. r2.9)'
        required: true
        type: string
      git_commit:
        description: 'Git commit to cherry-pick'

    initiates CI/CD tests.
-   We can't move forward if these tests fail.
-   In such situations, we may request you to make further changes to your PR
    for the tests to pass.
-   Once the tests pass, we now bring all the code into the internal code base,
    using a job called "copybara".

**5. Copy to Google Internal codebase and run internal CI**

-   Once the PR is in the Google codebase, we make sure it integrates well with

# Install Python packages for this container's version
cat >pythons.txt <<EOF
$VERSION
$VERSION-dev
$VERSION-venv
$VERSION-distutils
EOF
/setup.packages.sh pythons.txt

# Re-link pyconfig.h from aarch64-linux-gnu into the devtoolset directory
# for any Python version present
pushd /usr/include/aarch64-linux-gnu
for f in $(ls | grep python); do
  # set up symlink for devtoolset-10
  rm -f /dt10/usr/include/aarch64-linux-gnu/$f

# limitations under the License.
#
# ==============================================================================
#
# Repacks libtensorflow tarballs into $DIR with provided $TARBALL_SUFFIX,
# and also repacks libtensorflow-src.jar into a standardized format.

# Helper function to copy a srcjar after moving any source files
# directly under the root to the "maven-style" src/main/java layout
#

name: Trusted Partner PR
on:
  pull_request_target:

permissions:
  contents: read

jobs:
  assign-partner-tags:
    runs-on: ubuntu-latest
    permissions:
      # Needed to attach tags into the PR
      issues: write
      contents: write
      pull-requests: write
    if: |
      github.event.pull_request.draft == false &&
      github.event.sender.type == 'User'
    steps:

# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"
  docker run $TFCI_DOCKER_ARGS --name tf -w "$TFCI_GIT_DIR" -itd --rm \
      -v "$TFCI_GIT_DIR:$TFCI_GIT_DIR" \
      --env-file "$env_file" \

`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually

  // code that is layered on top of the runtime.
  virtual bool UsesTFRT() = 0;

  // List attributes of available devices
  virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;

  // Add `devices` into context's device manager. Context's device manager
  // will take ownership and maintain devices' lifetime.
  virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0;

# "pip tests" run a similar suite of tests the "nonpip" tests, but do something
# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests