* Written by Doug Lea with assistance from members of JCP JSR-166 Expert Group and released to
   * the public domain, as explained at http://creativecommons.org/publicdomain/zero/1.0/
   *
   * Other contributors include Andrew Wright, Jeffrey Hayes, Pat Fisher, Mike Judd.
   */

  /**
   * An implementation of {@link ExecutorService#invokeAny} for {@link ListeningExecutorService}
   * implementations.
   */
  @SuppressWarnings({

      successor(pred(), succ());
      if (left == null) {
        return right;
      } else if (right == null) {
        return left;
      } else if (left.height >= right.height) {
        AvlNode<E> newTop = pred();
        // newTop is the maximum node in my left subtree
        newTop.left = left.removeMax(newTop);
        newTop.right = right;
        newTop.distinctElements = distinctElements - 1;

    @Override
    public int compare(@CheckForNull Object left, @CheckForNull Object right) {
      if (left == right) {
        return 0;
      } else if (left == null) {
        return -1;
      } else if (right == null) {
        return 1;
      }
      int leftCode = identityHashCode(left);
      int rightCode = identityHashCode(right);
      if (leftCode != rightCode) {
        return leftCode < rightCode ? -1 : 1;

<img src="/img/tutorial/generate-clients/image06.png">

In this case you have:

* `ItemsService`
* `UsersService`

### Client Method Names

Right now the generated method names like `createItemItemsPost` don't look very clean:

```TypeScript
ItemsService.createItemItemsPost({name: "Plumbus", price: 5})
```

    *   `steps_per_epoch` and `steps` arguments are supported with numpy arrays.
    *   New error message when unexpected keys are used in
        sample_weight/class_weight dictionaries
    *   Losses are scaled in Keras compile/fit and not in the optimizers
        anymore. If you are using custom training loop, we have new utilities to

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/tiangolo/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>

---

"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/tiangolo/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>

---

"_Мы использовали библиотеку **FastAPI** для создания сервера **REST**, к которому можно делать запросы для получения **прогнозов**. [для Ludwig]_"

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/tiangolo/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>

---

"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"

<div style="text-align: right; margin-right: 10%;">Kabir Khan - <strong>Microsoft</strong> <a href="https://github.com/tiangolo/fastapi/pull/26" target="_blank"><small>(ref)</small></a></div>

---

"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"

     */
    long deadline = System.nanoTime() + unit.toNanos(timeout);
    int added = 0;
    while (added < numElements) {
      // we could rely solely on #poll, but #drainTo might be more efficient when there are multiple
      // elements already available (e.g. LinkedBlockingQueue#drainTo locks only once)
      added += q.drainTo(buffer, numElements - added);