// was some asymmetry so that the initial v0 and v1 differ from v2 and v3.
    private long v0 = 0x736f6d6570736575L;
    private long v1 = 0x646f72616e646f6dL;
    private long v2 = 0x6c7967656e657261L;
    private long v3 = 0x7465646279746573L;

    // The number of bytes in the input.
    private long b = 0;

    // The final 64-bit chunk includes the last 0 through 7 bytes of m followed by null bytes

    @Override public EventListener create(Call call) {
      long callId = nextCallId.getAndIncrement();
      System.out.printf("%04d %s%n", callId, call.request().url());
      return new PrintingEventListener(callId, System.nanoTime());
    }
  };

  final long callId;
  final long callStartNanos;

  public PrintingEventListener(long callId, long callStartNanos) {
    this.callId = callId;

  /* Helper function to make a Long value from an Integer. */
  private static final Function<Integer, Long> TO_LONG =
      new Function<Integer, Long>() {
        @Override
        public Long apply(Integer n) {
          return Long.valueOf(n);
        }
      };

  /* Helper function to make a BigInteger value from a Long. */
  private static final Function<Long, BigInteger> TO_BIGINTEGER =

                .exists("exists" + Long.toHexString(entropy.nextLong()))
                .missing("missing" + Long.toHexString(entropy.nextLong()))
                .build();
        ActivationProperty ap = ActivationProperty.newBuilder()
                .name("name" + Long.toHexString(entropy.nextLong()))
                .value("value" + Long.toHexString(entropy.nextLong()))
                .build();

            long totalSavings,
            List<ValueFrequency> mostFrequentValues) {}

    record PathAnalysis(
            String path,
            long uniqueStrings,
            long totalOccurrences,
            long totalMemory,
            long potentialSavings,
            double duplicationRatio,
            List<ValueFrequency> mostFrequentValues) {}

        "byte" to "Byte",
        "java.lang.Short" to "Short",
        "short" to "Short",
        "java.lang.Integer" to "Int",
        "int" to "Int",
        "java.lang.Long" to "Long",
        "long" to "Long",
        "java.lang.Float" to "Float",
        "float" to "Float",
        "java.lang.Double" to "Double",
        "double" to "Double"
    )

    }

    @ParameterizedTest
    @DisplayName("Test with various timeout values")
    @ValueSource(longs = { 0L, 1L, -1L, Long.MAX_VALUE, Long.MIN_VALUE, 1000L, -1000L })
    void testWithVariousTimeoutValues(long timeout) {
        // Setup
        String pipeName = "TestPipe";
        FsctlPipeWaitRequest request = new FsctlPipeWaitRequest(pipeName, timeout);

    protected long delayMillisAfterProcessing = 0L;

    /** Delay in milliseconds when no URL is in the queue */
    protected long delayMillisAtNoUrlInQueue = 500L;

    /** Delay in milliseconds before processing a URL */
    protected long delayMillisBeforeProcessing = 0L;

    /** Delay in milliseconds for waiting for new URLs */
    protected long delayMillisForWaitingNewUrl = 1000L;

    /**

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

## `async` and `await` { #async-and-await }

        return this;
    }

    public void setCreatedTime_Equal(Long createdTime) {
        setCreatedTime_Term(createdTime, null);
    }

    public void setCreatedTime_Equal(Long createdTime, ConditionOptionCall<TermQueryBuilder> opLambda) {
        setCreatedTime_Term(createdTime, opLambda);
    }

    public void setCreatedTime_Term(Long createdTime) {
        setCreatedTime_Term(createdTime, null);
    }