for (LoadingCache<Object, Object> cache : caches()) {
      cache.getUnchecked(1);
      cache.getUnchecked(2);

      Set<Object> keys = cache.asMap().keySet();
      // We don't know whether these are still in the cache, so we can't assert on the return
      // values of these removes, but the cache should be empty after the removes, regardless.
      keys.remove(1);
      keys.remove(2);
      assertFalse(keys.remove(null));

It had the best performance benchmarks at the time (only surpassed by Starlette).

At first, it didn't have an automatic API documentation web UI, but I knew I could add Swagger UI to it.

It had a dependency injection system. It required pre-registration of components, as other tools discussed above. But still, it was a great feature.

# TensorFlow continuous integration

> **Warning** This folder is still under construction. It is part of an ongoing
> effort to improve the structure of CI and build related files within the
> TensorFlow repo. This warning will be removed when the contents of this
> directory are stable and appropriate documentation around its usage is in
> place.

Maintainer: TensorFlow DevInfra

Like `item.model_dump(exclude_unset=True)`.

!!! info
    In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

    The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

    public BuildResumptionData(final List<String> remainingProjects) {
        this.remainingProjects = remainingProjects;
    }

    /**
     * Returns the projects that still need to be built when resuming.
     * @return A list containing the group and artifact id of the projects.
     */
    public List<String> getRemainingProjects() {
        return this.remainingProjects;
    }

 *       cancel} on the {@code Future}). So beware: <i>Even if you cancel every preceding {@code
 *       Future} returned by this class, the next task may still have to wait.</i>.
 *   <li>Once an {@code AsyncCallable} returns a {@code Future}, this class considers that task to
 *       be "done" as soon as <i>that</i> {@code Future} completes in any way. Notably, a {@code

	}
	if len(keys) != int(limit) {
		t.Fatalf("Expected length of the batch keys to be %v but got %v", limit, len(items))
	}
	batchLen = batch.Len()
	if batchLen != 0 {
		t.Fatalf("expected batch to be empty but still left with %d items", batchLen)
	}
	// Add duplicate entries
	for i := 0; i < 10; i++ {
		if err := batch.Add(99, 99); err != nil {
			t.Fatalf("failed to add duplicate item %v to batch after Get; %v", i, err)
		}
	}

When you return a `Response` directly its data is not validated, converted (serialized), nor documented automatically.

But you can still document it as described in [Additional Responses in OpenAPI](additional-responses.md){.internal-link target=_blank}.

       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
   

```Python
say_hi()  # Oh, no, this throws an error! 😱
```

The `name` parameter is **still required** (not *optional*) because it doesn't have a default value. Still, `name` accepts `None` as the value:

```Python
say_hi(name=None)  # This works, None is valid 🎉
```