/**
     * Creates a new MsPowerPointExtractor instance.
     */
    public MsPowerPointExtractor() {
        super();
    }

    /**
     * Extracts text from the PowerPoint input stream.
     * @param in The input stream.
     * @param params The parameters.
     * @return The extracted data.
     */
    @Override
    public ExtractData getText(final InputStream in, final Map<String, String> params) {

		[]byte("short"),
		[]byte(strings.Repeat("a", 42)),
	}
	for _, input := range cloneTests {
		clone := Clone(input)
		if !Equal(clone, input) {
			t.Errorf("Clone(%q) = %q; want %q", input, clone, input)
		}

		if input == nil && clone != nil {
			t.Errorf("Clone(%#v) return value should be equal to nil slice.", input)
		}

		if input != nil && clone == nil {

 * chain.add(new SomeNormalizer());
 * chain.add(new AnotherNormalizer());
 * String normalizedText = chain.normalize("input text", "field", "en");
 * </pre>
 *
 * <p>Methods:</p>
 * <ul>
 *   <li>{@link #normalize(String, String, String...)} - Applies all added normalizers to the input text.</li>
 *   <li>{@link #add(Normalizer)} - Adds a new normalizer to the chain.</li>
 * </ul>
 *
 * <p>Fields:</p>
 * <ul>

					<input class="form-control" type="text" id="as_q" name="as.q" value="${f:h(fe:join(as.q))}">
				</div>
			</div>
			<div class="mb-3 row">
				<label for="as_epq" class="col-lg-3 col-md-4 col-sm-5 col-12 col-form-label"><la:message
						key="labels.advance_search_phrase_query"
					/></label>
				<div class="col-lg-5 col-md-8 col-sm-7 col-6">

                                        <th style="width: 25%"><la:message
                                                key="labels.dict_stopwords_source"/></th>
                                        <td>${f:h(input)}<la:hidden property="input"/></td>
                                    </tr>
                                    </tbody>
                                </table>
                            </div>

import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */
@NullUnmarked

 * System.out.println(normalized); // Outputs: ガギグゲゴ
 * }
 * </pre>
 *
 * <p>Note: This class assumes that the input string contains valid Hankaku Kana characters
 * and does not perform any validation on the input.</p>
 *
 * @see Normalizer
 */
public class HankakuKanaToZenkakuKana implements Normalizer {
    /**
     * Constructs a new {@link HankakuKanaToZenkakuKana}.
     */

Add the following optimal entries for `hive-site.xml` to configure Hive with **MinIO**.

```
hive.blobstore.use.blobstore.as.scratchdir=true
hive.exec.input.listing.max.threads=50
hive.load.dynamic.partitions.thread=25
hive.metastore.fshandler.threads=50
hive.mv.files.threads=40
mapreduce.input.fileinputformat.list-status.num-threads=50
```

 * the License.
 */

/*
 * SipHash-c-d was designed by Jean-Philippe Aumasson and Daniel J. Bernstein and is described in
 * "SipHash: a fast short-input PRF" (available at https://131002.net/siphash/siphash.pdf).
 */

package com.google.common.hash;

import static com.google.common.base.Preconditions.checkArgument;

import com.google.errorprone.annotations.Immutable;

    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,
    std::unordered_map<const Node*, std::vector<int>>* input_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(