}

    @Override
    @ParametricNullness
    public T get() {
      // Another variant of Double Checked Locking.
      //
      // We use two volatile reads. We could reduce this to one by
      // putting our fields into a holder class, but (at least on x86)
      // the extra memory consumption and indirection are more
      // expensive than the extra volatile reads.

   * Nearly all declarations in this class are package-private,
   * accessed directly by subclasses.
   *
   * Table entries are of class Cell; a variant of AtomicLong padded
   * to reduce cache contention on most processors. Padding is
   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each

    hashed_password = hashed_password,
)
```

!!! warning
    The supporting additional functions are just to demo a possible flow of the data, but they of course are not providing any real security.

## Reduce duplication

Reducing code duplication is one of the core ideas in **FastAPI**.

`tf.expand_dims`: `dim` becomes `axis` * `tf.reduce_all`: `reduction_indices`
becomes `axis` * `tf.reduce_any`: `reduction_indices` becomes `axis` *
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:

 * </ul>
 *
 * <p>The one downside of {@code Stopwatch} relative to {@link System#nanoTime()} is that {@code
 * Stopwatch} requires object allocation and additional method calls, which can reduce the accuracy
 * of the elapsed times reported. {@code Stopwatch} is still suitable for logging and metrics where
 * reasonably accurate values are sufficient. If the uncommon case that you need to maximize

   * Nearly all declarations in this class are package-private,
   * accessed directly by subclasses.
   *
   * Table entries are of class Cell; a variant of AtomicLong padded
   * to reduce cache contention on most processors. Padding is
   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each

connecting to servers with buggy HTTP/2 implementations.
These kinds of changes are unavoidable and
[permitted by the Go 1 compatibility rules](/doc/go1compat).
Even so, Go provides a mechanism called GODEBUG to
reduce the impact such changes have on Go developers
using newer toolchains to compile old code.

A GODEBUG setting is a `key=value` pair
that controls the execution of certain parts of a Go program.

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,

	onlineDisks = make([]StorageAPI, len(disks))

	// List all the file commit ids from parts metadata.
	modTimes := listObjectModtimes(partsMetadata, errs)

	// Reduce list of UUIDs to a single common value.
	modTime = commonTime(modTimes, quorum)

	if modTime.IsZero() || modTime.Equal(timeSentinel) {
		etags := listObjectETags(partsMetadata, errs, quorum)

    Stream<String> streamB = Stream.of("b").onClose(closeCountB::incrementAndGet);
    Stream<String> concatenated =
        Stream.<Stream<String>>of(Stream.of("a"), streamB, Stream.empty(), Stream.of("c", "d"))
            .reduce(Stream.empty(), Stream::concat);
    assertThat(concatenated).containsExactly("a", "b", "c", "d").inOrder();
    concatenated.close();
    assertThat(closeCountB.get()).isEqualTo(1);
  }