tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python_bin=python3
  # TODO(belitskiy): Add a `python3` alias/symlink to Windows Docker image.
  if [[ $(uname -s) = MSYS_NT* ]]; then
    python_bin="python"
  fi
  tfrun "$python_bin" tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name

# If building installer wheels, set the required environment variables that are
# read by setup.py.
if [[ "$TFCI_INSTALLER_WHL_ENABLE" == 1 ]]; then
  export collaborator_build=True
  # If building nightly installer wheels, set the project name to
  # nightly equivalent.
  if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
    export TFCI_INSTALLER_WHL_PROJECT_NAME="$TFCI_INSTALLER_WHL_NIGHTLY_PROJECT_NAME"
  fi

            isRelease -> "promoteFinalRelease"
            else -> "promoteFinalBackportRelease"
        }

    private fun nightlyTaskName(prefix: String): String =
        when {
            isMaster -> "${prefix}Nightly"
            isRelease -> "${prefix}ReleaseNightly"
            else -> "${prefix}PatchReleaseNightly"
        }

        |           |--------- QuickFeedback
        |           |--------- ...
        |           |--------- ReadyForRelease
        |
        |---------- Promotion
        |           |--------- Publish Nightly Snapshot
        |           |--------- Publish Branch Snapshot
        |           |--------- ...
        |
        |---------- Util
                    |--------- ...

    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

  Please upgrade your code to TensorFlow 2.0:
    * https://www.tensorflow.org/guide/migrate

  Or install the latest stable TensorFlow 1.X release:

## Purpose

We do not want to microbenchmark everything but the kitchen sink and should typically rely on our
[macrobenchmarks](https://elasticsearch-benchmarks.elastic.co/app/kibana#/dashboard/Nightly-Benchmark-Overview) with
[Rally](http://github.com/elastic/rally). Microbenchmarks are intended to spot performance regressions in performance-critical components.

    PULL_REQUEST_FEEDBACK("Pull Request Feedback", "Run various functional tests", "PullRequestFeedback"),
    READY_FOR_NIGHTLY(
        "Ready for Nightly",
        "Rerun tests in different environments / 3rd party components",
        "ReadyforNightly",
    ),
    READY_FOR_RELEASE("Ready for Release", "Once a day: Rerun tests in more environments", "ReadyforRelease"),

# Release 2.12.0

### Breaking Changes

*   Build, Compilation and Packaging

 */
@GwtCompatible
@J2ktIncompatible // Super-sourced
abstract class GwtFluentFutureCatchingSpecialization<V extends @Nullable Object>
    extends AbstractFuture<V> {
  /*
   * This server copy of the class is empty. The corresponding GWT copy contains alternative
   * versions of catching() and catchingAsync() with slightly different signatures from the ones