if (state instanceof Blocker) {
        blocker = (Blocker) state;
      }
      spinCount++;
      if (spinCount > MAX_BUSY_WAIT_SPINS) {
        /*
         * If we have spun a lot, just park ourselves. This will save CPU while we wait for a slow
         * interrupting thread. In theory, interruptTask() should be very fast, but due to

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.

    public abstract Queue<Integer> create(Comparator<Integer> comparator);
  }

  /**
   * Does a CPU intensive operation on Integer and returns a BigInteger Used to implement an
   * ordering that spends a lot of cpu.
   */
  static class ExpensiveComputation implements Function<Integer, BigInteger> {
    @Override
    public BigInteger apply(Integer from) {
      BigInteger v = BigInteger.valueOf(from);

- Reduced server startup time. For IAM encryption with the root credentials, MinIO had
   to use a memory-hard function (Argon2) that (on purpose) consumes a lot of memory and CPU.
   The new KMS-based approach can use a key derivation function that is orders of magnitudes
   cheaper w.r.t. memory and CPU.
- Root credentials can now be changed easily. Before, a two-step process was required to

In Python 3.10, as an alternative to using the generics `Union` and `Optional`, you can use the <abbr title='also called "bitwise or operator", but that meaning is not relevant here'>vertical bar (`|`)</abbr> to declare unions of types, that's a lot better and simpler.

////

//// tab | Python 3.9+

You can use the same builtin types as generics (with square brackets and types inside):

* `list`
* `tuple`
* `set`
* `dict`

import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Benchmarks for various potential implementations of {@code ByteSource.asCharSource(...).read()}.
 */
// These benchmarks allocate a lot of data so use a large heap
@VmOptions({"-Xms12g", "-Xmx12g", "-d64"})
@NullUnmarked
public class ByteSourceAsCharSourceReadBenchmark {
  enum ReadStrategy {
    TO_BYTE_ARRAY_NEW_STRING {
      @Override

Downloading and installing the package dependencies **could take minutes**, but using the **cache** would **take seconds** at most.

And as you would be building the container image again and again during development to check that your code changes are working, there's a lot of accumulated time this would save.

                return String.valueOf(QUALIFIERS.indexOf(""));
            }

            int i = QUALIFIERS.indexOf(qualifier);

            // Just returning an Integer with the index here is faster, but requires a lot of if/then/else to check for
            // -1
            //  or QUALIFIERS.size and then resort to lexical ordering. Most comparisons are decided by the first
            // character,

## Documenting the callback { #documenting-the-callback }

The actual callback code will depend heavily on your own API app.

And it will probably vary a lot from one app to the next.

It could be just one or two lines of code, like:

```Python
callback_url = "https://example.com/api/v1/invoices/events/"

# Recommended: use a local+remote cache.
#
#   Bazel will cache your builds in tensorflow/build_output/cache,
#   and will also try using public build cache results to speed up
#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures