* Remove unnecessary override of /etc/init.d/docker on containervm image. ([#23593](https://github.com/kubernetes/kubernetes/pull/23593), [@dchen1107](https://github.com/dchen1107))
* Change kube-proxy & fluentd CPU request to 20m/80m. ([#23646](https://github.com/kubernetes/kubernetes/pull/23646), [@cjcullen](https://github.com/cjcullen))

  }

  /**
   * Creates a fluent iterable with the first {@code size} elements of this fluent iterable. If this
   * fluent iterable does not contain that many elements, the returned fluent iterable will have the
   * same behavior as this fluent iterable. The returned fluent iterable's iterator supports {@code
   * remove()} if this fluent iterable's iterator does.
   *

  }

  public void testToList() {
    assertEquals(Lists.newArrayList(1, 2, 3, 4), fluent(1, 2, 3, 4).toList());
  }

  public void testToList_empty() {
    assertTrue(fluent().toList().isEmpty());
  }

  public void testToSortedList_withComparator() {
    assertEquals(
        Lists.newArrayList(4, 3, 2, 1),
        fluent(4, 1, 3, 2).toSortedList(Ordering.<Integer>natural().reverse()));
  }

        assertSame(request, result); // Fluent API
        assertSame(proxy, request.proxy());
    }

    @Test
    public void testEncodingMethod() {
        CurlRequest request = new CurlRequest(Method.GET, "https://example.com");
        String encoding = "ISO-8859-1";

        CurlRequest result = request.encoding(encoding);

        assertSame(request, result); // Fluent API

        RequestData data = RequestDataBuilder.newRequestData().weight(2.5f).build();

        assertEquals(2.5f, data.getWeight(), 0.001f);
    }

    public void test_fluentApiChaining() {
        // Test fluent API method chaining
        RequestData data =
                RequestDataBuilder.newRequestData().method(Constants.GET_METHOD).url("https://example.com/page").weight(1.5f).build();

import com.google.common.primitives.Doubles;
import com.google.common.primitives.Ints;
import java.math.RoundingMode;
import java.util.Collection;
import java.util.LinkedHashMap;
import java.util.Map;

/**
 * Provides a fluent API for calculating <a
 * href="http://en.wikipedia.org/wiki/Quantile">quantiles</a>.
 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);

 * and cache file size to optimize the extraction process.
 *
 * <p>
 * The main purpose of this class is to simplify the extraction process by providing a fluent interface
 * for configuring the extraction parameters and handling the underlying complexities of content processing,
 * such as MIME type detection, extractor selection, and content length validation.
 * </p>
 *
 * <p>

**Factory**: `CrawlerClientFactory`, `ExtractorFactory` - protocol/format-specific component selection
**Strategy**: `CrawlerClient`, `Extractor`, `Transformer` - pluggable implementations
**Builder**: `RequestDataBuilder`, `ExtractorBuilder` - fluent construction
**Template Method**: `AbstractCrawlerClient`, `AbstractExtractor` - common logic with overrides
**DI**: LastaFlute container with `@Resource` and XML config

### Core Principles

**Thread Safety**:

 * specific language governing permissions and limitations
 * under the License.
 */
package org.fusesource.jansi;

import java.util.ArrayList;

/**
 * Provides a fluent API for generating
 * <a href="https://en.wikipedia.org/wiki/ANSI_escape_code#CSI_sequences">ANSI escape sequences</a>.
 *
 * This class comes from Jansi and is provided for backward compatibility

 *   <li>Optionally removing duplicate terms based on a flag.</li>
 *   <li>Limiting the maximum size of alphanumeric and symbol terms.</li>
 * </ul>
 *
 * <p>The {@link TextNormalizeContext} class provides a fluent API to configure the text
 * normalization process, including setting initial buffer capacity, maximum term sizes,
 * duplicate term removal, and custom space characters.
 *
 * <p>Example usage:
 * <pre>{@code