{"name": "Holy Buddies", "description": None},
            ],
        },
        {
            "name": "System of an Up",
            "items": [
                {
                    "name": "Salt",
                    "description": "The kombucha mushroom people's favorite",
                },
                {"name": "Pad Thai", "description": None},
                {
                    "name": "Lonely Night",

However, there is also a bit of additional logic. Gateway types have extensive status reporting. Unlike Ingress, this is status reporting is done inline in the main controller, allowing status generation to be done directly in the logic processing the resources.

Additionally, Gateway involves two components writing to the cluster:

     *
     * @see #getModelIds()
     * @param modelId The identifier of the model whose active profiles should be retrieved, must not be {@code null}.
     * @return The active profiles of the model or an empty list if none or {@code null} if the specified model id does
     *         not refer to a known model.
     */
    List<Profile> getActivePomProfiles(String modelId);

    /**

     * Gets the installation Toolchains source.
     *
     * @return the installation Toolchains source or {@code null} if none
     */
    @Nonnull
    Optional<Source> getInstallationToolchainsSource();

    /**
     * Gets the user Toolchains source.
     *
     * @return the user Toolchains source or {@code null} if none
     */
    @Nonnull
    Optional<Source> getUserToolchainsSource();

    @Nonnull

	# Iterate down a (possible) chain of symlinks
	while [ -L "$TARGET_FILE" ]; do
		TARGET_FILE=$(env readlink $TARGET_FILE)
		cd $(dirname $TARGET_FILE)
		TARGET_FILE=$(basename $TARGET_FILE)
	done

	# Compute the canonicalized name by finding the physical path
	# for the directory we're in and appending the target file.
	PHYS_DIR=$(pwd -P)
	RESULT=$PHYS_DIR/$TARGET_FILE
	echo $RESULT
}

## FIXME:

def say_hi(name: str | None):

# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,
# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 
# Pass --config=pip to run the same suite of tests. If you want to run just one
# test for investigation, you'll need --config=pip_venv instead, and then you

	args1+=("http://localhost:$((9000 + i))/tmp/xl/1/$i ")
	args2+=("http://localhost:$((9100 + i))/tmp/xl/2/$i ")
done

for i in $(seq 1 $NODES); do
	./minio server --address "127.0.0.1:$((9000 + i))" ${args1[@]} & # | tee /tmp/minio/node.$i &
	./minio server --address "127.0.0.1:$((9100 + i))" ${args2[@]} & # | tee /tmp/minio/node.$i &
done

sleep 10

./mc alias set myminio1 http://localhost:9001 minioadmin minioadmin

		// current stack snapshot matches the initial one, no leaks, return.
		if len(leaked) == 0 {
			return
		}
		// wait a test again will deadline.
		if UTCNow().Before(deadline) {
			time.Sleep(leakDetectPauseTimeMs * time.Millisecond)
			continue
		}
		// after the deadline time report all the difference in the latest snapshot compared with the initial one.
		for _, g := range leaked {

	for i := 0; i < resyncParallelRoutines; i++ {
		wg.Add(1)
		workers[i] = make(chan ReplicateObjectInfo, 100)
		i := i
		go func(ctx context.Context, idx int) {
			defer wg.Done()
			for roi := range workers[idx] {
				select {
				case <-ctx.Done():
					return
				case <-s.resyncCancelCh:
				default:
				}
				traceFn := s.trace(tgt.ResetID, fmt.Sprintf("%s/%s (%s)", opts.bucket, roi.Name, roi.VersionID))