* Since Quantiles provides a fluent-style API, each test covers a chain of methods resulting in
   * the computation of one or more quantiles (or in an error) rather than individual methods. The
   * tests are divided into three sections:
   * 1. Tests on a hardcoded dataset for chains starting with median(), quartiles(), and scale(10);
   * 2. Tests on hardcoded datasets include non-finite values for chains starting with scale(10);

   * Since Quantiles provides a fluent-style API, each test covers a chain of methods resulting in
   * the computation of one or more quantiles (or in an error) rather than individual methods. The
   * tests are divided into three sections:
   * 1. Tests on a hardcoded dataset for chains starting with median(), quartiles(), and scale(10);
   * 2. Tests on hardcoded datasets include non-finite values for chains starting with scale(10);

        FilterChain chain = createDefaultFilterChain();
        for (final Filter element : filterList) {
            chain = appendFilterChain(element, chain);
        }
        filterChain = chain;
    }

    /**
     * Appends a filter to an existing filter chain.
     *
     * @param filter the filter to append
     * @param chain the existing filter chain to append to

- Added support for CEL expressions with escaped names in the structured authentication config. Using `[...]` to access claims or user data was recommended when names contained characters that would otherwise need escaping. CEL optionals with `?` could be used where has was not applicable — for example, `claims[?"kubernetes.io"]` or `user.extra[?"domain.io/foo"]`. ([#131574](https://github.com/kubernetes/kubernetes/pull/131574), [@enj](https://github.com/enj)) [SIG...

        chain.add(normalizer1);
        chain.add(normalizer2);
        chain.add(normalizer3);

        String result = chain.normalize("test", "field");

        assertEquals("test-1-2-3", result);
    }

    @Test
    public void test_chainWithCustomNormalizer() throws Exception {
        NormalizerChain chain = new NormalizerChain();
        chain.add((text, field, langs) -> text.trim());

            break;
        case FAVORITE:
            processFavoriteRequest(request, response, chain);
            break;
        case FAVORITES:
            processFavoritesRequest(request, response, chain);
            break;
        case PING:
            processPingRequest(request, response, chain);
            break;
        case SCROLL:

 * It applies each normalizer in the order they were added to the chain.
 *
 * <p>Usage example:</p>
 * <pre>
 * NormalizerChain chain = new NormalizerChain();
 * chain.add(new SomeNormalizer());
 * chain.add(new AnotherNormalizer());
 * String normalizedText = chain.normalize("input text", "field", "en");
 * </pre>
 *
 * <p>Methods:</p>
 * <ul>

        final ReadingConverterChain chain = new ReadingConverterChain();
        chain.addConverter(new AnalyzerConverter(client, settings));
        chain.addConverter(new KatakanaToAlphabetConverter());
        return chain;
    }

    /**
     * Creates a default ReadingConverterChain with a KatakanaToAlphabetConverter.
     *

            normalizer = new LangAnalyzerNormalizer(null);
        } else {
            final NormalizerChain chain = new NormalizerChain();
            for (final String lang : langs) {
                chain.add(new LangAnalyzerNormalizer(lang));
            }
            normalizer = chain;
        }
        return normalizer.normalize(text, field);
    }

    /**
     * Language-specific analyzer normalizer.

import java.util.List;
import java.util.Set;

/**
 * A chain of {@link ReadingConverter} implementations that applies each converter in sequence to generate possible reading variations of a given text.
 * It maintains a list of ReadingConverter instances and iterates through them, applying each converter to the input text and accumulating the results.
 * The chain stops processing when the maximum number of readings is reached.
 */