import org.checkerframework.checker.nullness.qual.NonNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A strategy for determining whether two instances are considered equivalent, and for computing
 * hash codes in a manner consistent with that equivalence. Two examples of equivalences are the
 * {@linkplain #identity() identity equivalence} and the {@linkplain #equals "equals" equivalence}.
 *

   * com.google.common.primitives.UnsignedLongs}'s encoding of 64-bit unsigned numbers).
   *
   * <p>This function is best understood as a <a
   * href="https://en.wikipedia.org/wiki/Fingerprint_(computing)">fingerprint</a> rather than a true
   * <a href="https://en.wikipedia.org/wiki/Hash_function">hash function</a>.
   *
   * @since 20.0
   */
  public static HashFunction farmHashFingerprint64() {

#### `lru_cache` Technical Details

`@lru_cache` modifies the function it decorates to return the same value that was returned the first time, instead of computing it again, executing the code of the function every time.

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.
* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

These examples show when a Go implementation can use that instruction:
</p>

<pre>
// FMA allowed for computing r, because x*y is not explicitly rounded:
r  = x*y + z
r  = z;   r += x*y
t  = x*y; r = t + z
*p = x*y; r = *p + z
r  = x*y + float64(z)

// FMA disallowed for computing r, because it would omit rounding of x*y:
r  = float64(x*y) + z
r  = z; r += float64(x*y)
t  = float64(x*y); r = t + z

These examples show when a Go implementation can use that instruction:
</p>

<pre>
// FMA allowed for computing r, because x*y is not explicitly rounded:
r  = x*y + z
r  = z;   r += x*y
t  = x*y; r = t + z
*p = x*y; r = *p + z
r  = x*y + float64(z)

// FMA disallowed for computing r, because it would omit rounding of x*y:
r  = float64(x*y) + z
r  = z; r += float64(x*y)
t  = float64(x*y); r = t + z

 *   Updated `tf.keras.layers.BatchNormalization` to support masking of the inputs (`mask` argument) when computing the mean and variance.
 *   Add `tf.keras.layers.Identity`, a placeholder pass-through layer.
 *   Add `show_trainable` option to `tf.keras.utils.model_to_dot` to display layer trainable status in model plots.

    * If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
* **Starlette**:

                if (res != null && !res.isEmpty()) {
                    List<Binding<Q>> bindingList = new ArrayList<>(res);
                    Comparator<Binding<Q>> comparing = Comparator.comparing(Binding::getPriority);
                    bindingList.sort(comparing.reversed());
                    Binding<Q> binding = bindingList.get(0);
                    return compile(binding);
                }

- Fixed computing backoff delay when using Job pod failure policy, by including in the backoff delay calculation pod failures ignored from the backoffLimit counter. ([#119434](https://github.com/kubernetes/kubernetes/pull/119434), [@mimowo](https://github.com/mimowo))...