various sources must be consolidated and injected using the container.

  Currently, mojo configuration is done externally to the container, in the
  DefaultPluginManager in the maven-core API. In order to get from here to
  there, we need to do several things to add capability to the default
  configuration of plexus. This document will detail those changes.

Container Enhancements

* ExpressionEvaluator

    steps:
      - name: Checkout
        uses: actions/checkout@v6

      - name: Configure JDK
        uses: actions/setup-java@v5
        with:
          distribution: 'zulu'
          java-version: 17

      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v5

      - name: Run Container Tests

```sh
docker ps -a
```

`-a` flag makes sure you get all the containers (Created, Running, Exited). Then identify the `Container ID` from the output.

### Starting and Stopping Containers

To start a stopped container, you can use the [`docker start`](https://docs.docker.com/engine/reference/commandline/start/) command.

```sh
docker start <container_id>
```

@Named
@Singleton
public class DefaultLookup implements Lookup {

    private final PlexusContainer container;

    @Inject
    public DefaultLookup(PlexusContainer container) {
        this.container = container;
    }

    @Override
    public <T> T lookup(Class<T> type) {
        try {
            return container.lookup(type);
        } catch (ComponentLookupException e) {
            throw new LookupException(e);

    docker push "$TFCI_DOCKER_IMAGE"
  fi
fi

# Keep the existing "tf" container if it's already present.
# 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"

  if [[ $(uname -s) == MSYS_NT* ]]; then

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

        return attrs;
    }
}

/**
* HTML API: If current script has container(s) specified, initialise Termynal.
*/
if (document.currentScript.hasAttribute('data-termynal-container')) {
    const containers = document.currentScript.getAttribute('data-termynal-container');
    containers.split('|')
        .forEach(container => new Termynal(container))

 * encompasses both of these, 'container' is used.
 *
 * @author George van den Driessche
 */
@GwtCompatible
@NullMarked
public interface TestContainerGenerator<T, E extends @Nullable Object> {
  /** Returns the sample elements that this generate populates its container with. */
  SampleElements<E> samples();

  /**

# ==============================================================================
cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

TFCI_WHL_SIZE_LIMIT=450M
TFCI_WHL_SIZE_LIMIT_ENABLE=1
TFCI_WHL_IMPORT_TEST_ENABLE=1
TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS=""

# Used to simulate a T:\ drive within the container, to a limited extent,
# via a symlink.
# Helpful since the internal CI utilizes a T:\ drive, part of which is mounted
# to the container, and would result in C:\<path> != T:\<path> mismatches,
# when using variables like `TFCI_OUTPUT_DIR` in `docker exec commands,