*/
  public final Optional<@NonNull E> firstMatch(Predicate<? super E> predicate) {
    // Unsafe, but we can't do much about it now.
    return Iterables.<@NonNull E>tryFind((Iterable<@NonNull E>) getDelegate(), predicate);
  }

  /**
   * Returns a fluent iterable that applies {@code function} to each element of this fluent
   * iterable.
   *

Having the return model ensure that a value is always available and always `int` (not `None`) is very useful for the API clients, they can write much simpler code having this certainty.

Also, **automatically generated clients** will have simpler interfaces, so that the developers communicating with your API can have a much better time working with your API. 😎

///

    assert response.status_code == 200, response.text
    assert response.json() == data


def test_model_with_tuple_invalid():
    data = {"items": [["foo", "bar"], ["baz", "whatelse", "too", "much"]]}
    response = client.post("/model-with-tuple/", json=data)
    assert response.status_code == 422, response.text

    data = {"items": [["foo", "bar"], ["baz"]]}
    response = client.post("/model-with-tuple/", json=data)

  All of these operations will produce their own unique errors in the absence of
  a coordinated offline strategy. In addition, efforts to unite these failing
  behaviors behind a consistent user interface is much, much more difficult if
  the system can't tell whether it has access to the network required by these
  operations.

  Offline mode really means anticipating a lack of network connectivity, and as

Import the `Response` class (sub-class) you want to use and declare it in the *path operation decorator*.

For large responses, returning a `Response` directly is much faster than returning a dictionary.

Many packages that simplify it a lot have to make many compromises with the data model, database, and available features. And some of these packages that simplify things too much actually have security flaws underneath.

---

**FastAPI** doesn't make any compromise with any database, data model or tool.

It gives you all the flexibility to choose the ones that fit your project the best.

   * remaining 7.0 permits, and the remaining 3.0, we serve them by fresh permits produced by the
   * rate limiter.
   *
   * We already know how much time it takes to serve 3 fresh permits: if the rate is
   * "1 token per second", then this will take 3 seconds. But what does it mean to serve 7 stored

      // timed out. e.g. if we weren't event able to grab the lock within the timeout we would never
      // even check the guard. I don't think we care too much about this use case but it could lead
      // to a confusing error message.
      throw new TimeoutException("Timed out waiting for " + this + " to reach the RUNNING state.");
    }
  }

  @Override

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.

How much of the system resources do you want to be consuming/utilizing? It might be easy to think "not much", but in reality, you will probably want to consume **as much as possible without crashing**.

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

Containers also have their own **isolated** running processes (commonly just one process), file system, and network, simplifying deployment, security, development, etc.