* the uniformly-distributed random integer, then truncates any fractional
     * part, so higher integers would appear (in this case linearly) more often
     * than lower ones.
     */
    return (int) Math.pow(a, 1.0 / concentration);
  }

  @AfterExperiment
  void tearDown() {
    double req = requests.get();
    double hit = req - misses.get();

  /**
   * When we need a new thread to run tasks, we call [Backend.execute]. A few microseconds later we
   * expect a newly-started thread to call [Runnable.run]. We shouldn't request new threads until
   * the already-requested ones are in service, otherwise we might create more threads than we need.
   *
   * We use [executeCallCount] and [runCallCount] to defend against starting more threads than we
   * need. Both fields are guarded by `this`.
   */

   * invoked in the same order as the underlying service enters those states. Additionally, at most
   * one of the listener's callbacks will execute at once. However, multiple listeners' callbacks
   * may execute concurrently, and listeners may execute in an order different from the one in which
   * they were registered.
   *
   * <p>RuntimeExceptions thrown by a listener will be caught and logged. Any exception thrown

`secrets.compare_digest()` needs to take `bytes` or a `str` that only contains ASCII characters (the ones in English), this means it wouldn't work with characters like `á`, as in `Sebastián`.

To handle that, we first convert the `username` and `password` to `bytes` encoding them with UTF-8.

   *         .build();
   * }
   *
   * <p>Builder instances can be reused; it is safe to call {@link #build} multiple times to build
   * multiple graphs in series. Each new graph contains all the elements of the ones created before
   * it.
   *
   * @since 28.0
   */
  public static class Builder<N, V> {

    private final MutableValueGraph<N, V> mutableValueGraph;

This can be used as a quick shortcut if you have only one Pydantic model and want to remove some data from the output.

/// tip

But it is still recommended to use the ideas above, using multiple classes, instead of these parameters.

`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Create a PassLib "context". This is what will be used to hash and verify passwords.

/// tip

The PassLib context also has functionality to use different hashing algorithms, including deprecated old ones only to allow verifying them, etc.

For example, you could use it to read and verify passwords generated by another system (like Django) but hash any new passwords with a different algorithm like Bcrypt.

     * <br/>
     * Supported filters:
     * <ul>
     *     <li>"h" or "h(num)" - highest version or top list of highest ones filter</li>
     *     <li>"l" or "l(num)" - lowest version or bottom list of lowest ones filter</li>
     *     <li>"s" - contextual snapshot filter</li>
     *     <li>"ns" - unconditional snapshot filter (no snapshots selected from ranges)</li>

   * invoked in the same order as the underlying service enters those states. Additionally, at most
   * one of the listener's callbacks will execute at once. However, multiple listeners' callbacks
   * may execute concurrently, and listeners may execute in an order different from the one in which
   * they were registered.
   *
   * <p>RuntimeExceptions thrown by a listener will be caught and logged. Any exception thrown