func renderMatches(trafficMatches []*v1alpha3.HTTPMatchRequest) string {
	if len(trafficMatches) == 0 {
		return "everything"
	}

	matches := []string{}
	for _, trafficMatch := range trafficMatches {
		matches = append(matches, renderMatch(trafficMatch))
	}
	return strings.Join(matches, ", ")
}

func renderMatch(match *v1alpha3.HTTPMatchRequest) string {
	retval := "Match: "

  rpm2cpio "devtoolset-9-gcc-9.3.1-2.2.el7.src.rpm" |cpio -idmv
  tar -xvf "gcc-9.3.1-20200408.tar.xz" --strip 1
  ;;
esac

# Apply the devtoolset patches to gcc.
/rpm-patch.sh "gcc.spec"

./contrib/download_prerequisites

mkdir -p "${TARGET}-build"
cd "${TARGET}-build"

"${TARGET}-src/configure" \
      --prefix=/"${TARGET}/usr" \

    assertTrue(matcher.matches(s.charAt(0)));
    assertTrue(matcher.apply(s.charAt(0)));
    assertEquals(0, matcher.indexIn(s));
    assertEquals(0, matcher.indexIn(s, 0));
    assertEquals(-1, matcher.indexIn(s, 1));
    assertEquals(0, matcher.lastIndexIn(s));
    assertTrue(matcher.matchesAnyOf(s));
    assertTrue(matcher.matchesAllOf(s));
    assertFalse(matcher.matchesNoneOf(s));

#### CRD Watcher

For watches against custom types (CRDs), we want to gracefully handle missing CRDs. Naively starting informers against the missing types would result in errors and blocking startup. Instead, we introduce a "CRD Watcher" component that watches the CRDs in the cluster to determine if they are available or not.

        assertFalse(auth.matches("ftp://hostname:111/"));
        assertFalse(auth.matches("ftp://hostname:xx/"));
        assertFalse(auth.matches("ftp://hoge/test/aaa.html"));
        assertFalse(auth.matches("ftp://hoge/test"));
        assertFalse(auth.matches("ftp://hoge/"));
        assertFalse(auth.matches("ftp://hoge"));
        assertFalse(auth.matches("ftp://"));
        assertFalse(auth.matches("http://hostname/"));

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

    checkArgument(
        !separatorPattern.matcher("").matches(),
        "The pattern may not match the empty string: %s",
        separatorPattern);

    return new Splitter(
        new Strategy() {
          @Override
          public SplittingIterator iterator(final Splitter splitter, CharSequence toSplit) {
            final CommonMatcher matcher = separatorPattern.matcher(toSplit);

import javassist.CtClass

import java.util.regex.Pattern

/**
 * Matches JVM anonymous classes.
 */
class AnonymousClassesFilter implements ClassFilter {

    private static final Pattern PATTERN = Pattern.compile('.*\\$[0-9]+$')

    @Override
    boolean matches(CtClass ctClass) {
        return ctClass.name.matches(PATTERN)
    }

    public boolean isSnapshot(String version) {
        return checkSnapshot(version);
    }

    public static boolean checkSnapshot(String version) {
        return version.endsWith(SNAPSHOT) || SNAPSHOT_TIMESTAMP.matcher(version).matches();
    }

    @Override
    public VersionConstraint parseVersionConstraint(String constraint) {
        requireNonNull(constraint, "constraint");

	if err != nil {
		t.Fatal("Failed to parse batch-job-expire yaml", err)
	}
	if !slices.Equal(job.Expire.Prefix.F(), []string{"myprefix"}) {
		t.Fatal("Failed to parse batch-job-expire yaml")
	}

	multiPrefixExpireYaml := `
expire: # Expire objects that match a condition
  apiVersion: v1
  bucket: mybucket # Bucket where this batch job will expire matching objects from