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
only a placeholder to enable test cases to run. The TensorFlow build replaces
this file with a file generated from [`api_template.__init__.py`](https://www.github.com/tensorflow/tensorflow/blob/master/tensorflow/api_template.__init__.py)
"""

// error information in *status.
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig(
    TFE_ContextOptions* options, const void* proto, size_t proto_len,
    TF_Status* status);

// Controls how to act when we try to run an operation on a given device but
// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {

TensorFlow's official CI jobs run the scripts in this folder. Our internal CI
system, Kokoro, schedules our CI jobs by combining a build script with a file
from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
    ```

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

# 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:
      # We use this instead of the "run on branch" argument because GitHub looks

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# script to run pip-compile for keras, tensorboard, estimator deps.
# if there is a change in requirements.in then all lock files will be updated
# accordingly.

# -e: abort script if one command fails
# -u: error if undefined variable used

4) Add the version to `SUPPORTED_VERSIONS` in `updater.sh`, and
   `release_updater.sh`

5) Run the `updater.sh` shell script. \
   If the base requirements file hasn't yet been updated to account for the new
   Python version, which will require different versions for at least some
   dependencies, it will need to be updated now, for the script to run
   successfully.

```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.

  targets_.clear();
  for (TF_Operation* t : targets) {
    targets_.emplace_back(t);
  }
}

void CSession::Run(TF_Status* s) {
  if (inputs_.size() != input_values_.size()) {
    ADD_FAILURE() << "Call SetInputs() before Run()";
    return;
  }
  ResetOutputValues();
  output_values_.resize(outputs_.size(), nullptr);

*   TensorBoard fixes: graphs with only one data point, Nan values, reload
    button and auto-reload, tooltips in scalar charts, run filtering, stable
    colors
*   Tensorboard graph visualizer now supports run metadata. Clicking on nodes
    while viewing a stats for a particular run will show runtime statistics,
    such as memory or compute usage. Unused nodes will be faded out.

## Thanks to our Contributors