// 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");

    you may be facing one of the following issues:

    *   **Checkpoint loading failure.** The new optimizer handles optimizer
        state differently from the old optimizer, which simplies the logic of
        checkpoint saving/loading, but at the cost of breaking checkpoint
        backward compatibility in some cases. If you want to keep using an old
        checkpoint, please change your optimizer to

          () -> {
            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

   * 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");

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).

		p.errorf("TEXT %q: ABIInternal requires NOSPLIT", name)
	}

	// Next operand is the frame and arg size.
	// Bizarre syntax: $frameSize-argSize is two words, not subtraction.
	// Both frameSize and argSize must be simple integers; only frameSize
	// can be negative.
	// The "-argSize" may be missing; if so, set it to objabi.ArgsSizeUnknown.
	// Parse left to right.
	op := operands[next]
	if len(op) < 2 || op[0].ScanToken != '$' {

            lock.lock();
            latch.countDown();
            try {
              Thread.sleep(unit.toMillis(duration));
            } catch (InterruptedException e) {
              // simply finish execution
            } finally {
              lock.unlock();
            }
          }
        };
    thread.setDaemon(true);
    thread.start();
    awaitUninterruptibly(latch);

        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)",

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;

  TF_DeleteGraph(g1);
  TF_DeleteGraph(g2);
  TF_DeleteStatus(s);
}
*/

TEST(CAPI, ImportGraphDef) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create a simple graph.
  Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
  TF_Operation* oper = ScalarConst(3, graph, s);