() -> {
            try {
              lock.lock();
              try {
                if (state() != State.STOPPING) {
                  // This means that the state has changed since we were scheduled. This implies
                  // that an execution of runOneIteration has thrown an exception and we have
                  // transitioned to a failed state, also this means that shutDown has already

   * @param oldCount the expected present count of the element in this multiset
   * @param newCount the desired count of the element in this multiset
   * @return {@code true} if the condition for modification was met. This implies that the multiset
   *     was indeed modified, unless {@code oldCount == newCount}.
   * @throws IllegalArgumentException if {@code oldCount} or {@code newCount} is negative

          () -> {
            try {
              lock.lock();
              try {
                if (state() != State.STOPPING) {
                  // This means that the state has changed since we were scheduled. This implies
                  // that an execution of runOneIteration has thrown an exception and we have
                  // transitioned to a failed state, also this means that shutDown has already

        // Verify a sample of encrypted messages can be decrypted (to avoid extensive decryption that might fail)
        int sampleSize = Math.min(5, encrypted.size());
        for (int i = 0; i < sampleSize; i++) {
            try {
                byte[] decrypted = context.decryptMessage(encrypted.get(i));
                assertArrayEquals(messages.get(i).getBytes(), decrypted, "Sample message " + i + " should decrypt correctly");

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

        return iterator.hasNext();
      }

      @Override
      @ParametricNullness
      public T next() {
        return iterator.next();
      }
    };
  }

  /**
   * Simply returns its argument.
   *
   * @deprecated no need to use this
   * @since 10.0
   */
  @InlineMe(
      replacement = "checkNotNull(iterator)",

   * between elements will be made, but the order in which those pairs of elements are passed to the
   * consumer is <i>not</i> defined.
   *
   * <p>Note that many usages of this method can be replaced with simpler calls to {@link #zip}.
   * This method behaves equivalently to {@linkplain #zip zipping} the stream elements into
   * temporary pair objects and then using {@link Stream#forEach} on that stream.
   *

Pero también puedes explotar los beneficios del paralelismo y la multiprocesamiento (tener múltiples procesos ejecutándose en paralelo) para cargas de trabajo **CPU bound** como las de los sistemas de Machine Learning.

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

   * serialized with XML 1.0.
   *
   * <p>For SOAP 1.1 messages, see {@code XML_UTF_8} per <a
   * href="http://www.w3.org/TR/2000/NOTE-SOAP-20000508/">W3C Note on Simple Object Access Protocol
   * (SOAP) 1.1</a>
   *
   * @since 20.0
   */
  public static final MediaType SOAP_XML_UTF_8 = createConstantUtf8(APPLICATION_TYPE, "soap+xml");