* #queue}, the Executor will complete its tasks, and then restore the interruption. This means
     * that once the Thread returns to the Executor that this Executor composes, the interruption
     * will still be present. If the composed Executor is an ExecutorService, it can respond to
     * shutdown() by returning tasks queued on that Thread after {@link #worker} drains the queue.
     */

     * #queue}, the Executor will complete its tasks, and then restore the interruption. This means
     * that once the Thread returns to the Executor that this Executor composes, the interruption
     * will still be present. If the composed Executor is an ExecutorService, it can respond to
     * shutdown() by returning tasks queued on that Thread after {@link #worker} drains the queue.
     */

    val dateFormat =
      SimpleDateFormat("yyyyMMddHHmmss'Z'").apply {
        timeZone = utc
      }

    return dateFormat.format(date)
  }

  /**
   * Returns a composite adapter for a struct or data class. This may be used for both SEQUENCE and
   * SET types.
   *
   * The fields are specified as a list of member adapters. When decoding, a value for each

<pre>
tmp := [n]T{x1, x2, … xn}
tmp[0 : n]
</pre>

<p>
Within a composite literal of array, slice, or map type <code>T</code>,
elements or map keys that are themselves composite literals may elide the respective
literal type if it is identical to the element or key type of <code>T</code>.
Similarly, elements or keys that are addresses of composite literals may elide
the <code>&amp;T</code> when the element or key type is <code>*T</code>.

    val sampleValues = sampleValuesMap()

    val solo = EventRecorder(enforceOrder = false)
    val left = EventRecorder(enforceOrder = false)
    val right = EventRecorder(enforceOrder = false)
    val composite = left.eventListener + right.eventListener

    for (method in EventListener::class.java.declaredMethods) {
      if (method.name == "plus") continue

      val args =
        method.parameters

  override fun timeout(): Timeout = timeout

  override fun addEventListener(eventListener: EventListener) {
    // Atomically replace the current eventListener with a composite one.
    do {
      val previous = this.eventListener
    } while (!eventListenerUpdater.compareAndSet(this, previous, previous + eventListener))
  }

PossiblyPrime W = 55 B = 48 # Other witnesses for 65 and 85 will report composite: # Found non-trivial square root Result = Composite W = 41 B = 2c # End of iteration Result = Composite W = 41 B = 16 # End of iteration Result = Composite W = 41 B = 14 # End of iteration Result = Composite W = 41 B = 2 # End of iteration Result = Composite W = 41 B = 3a # End of iteration Result = Composite W = 55 B = 40 # End of iteration Result = Composite W = 55 B = 7 # End of iteration Result = Composite W = 55 B = 23...

PossiblyPrime W = 55 B = 48 # Other witnesses for 65 and 85 will report composite: # Found non-trivial square root Result = Composite W = 41 B = 2c # End of iteration Result = Composite W = 41 B = 16 # End of iteration Result = Composite W = 41 B = 14 # End of iteration Result = Composite W = 41 B = 2 # End of iteration Result = Composite W = 41 B = 3a # End of iteration Result = Composite W = 55 B = 40 # End of iteration Result = Composite W = 55 B = 7 # End of iteration Result = Composite W = 55 B = 23...

    FileEntry[] doMsrpcShareEnum() throws IOException {
        MsrpcShareEnum rpc;
        DcerpcHandle handle;

        rpc = new MsrpcShareEnum(url.getHost());

        /* JCIFS will build a composite list of shares if the target host has
         * multiple IP addresses such as when domain-based DFS is in play. Because
         * of this, to ensure that we query each IP individually without re-resolving

        This calling convention is now the preferred way to use concrete
        functions with nested values and composite tensors. Please check the
        [guide](https://www.tensorflow.org/guide/concrete_function) for more
        details on `concrete_ function`.
    *   Update `tf.function`'s `experimental_relax_shapes` to handle composite
        tensors appropriately.
    *   Optimize `tf.function` invocation, by removing redundant list converter.