// Now we hit the cap
    assertEquals(1 << 30, ImmutableSet.chooseTableSize(1 << 29));
    assertEquals(1 << 30, ImmutableSet.chooseTableSize((1 << 30) - 1));

    // Now we've gone too far
    assertThrows(IllegalArgumentException.class, () -> ImmutableSet.chooseTableSize(1 << 30));
  }

  @GwtIncompatible // RegularImmutableSet.table not in emulation
  public void testResizeTable() {

    // Now we hit the cap
    assertEquals(1 << 30, ImmutableSet.chooseTableSize(1 << 29));
    assertEquals(1 << 30, ImmutableSet.chooseTableSize((1 << 30) - 1));

    // Now we've gone too far
    assertThrows(IllegalArgumentException.class, () -> ImmutableSet.chooseTableSize(1 << 30));
  }

  @GwtIncompatible // RegularImmutableSet.table not in emulation
  public void testResizeTable() {

            Files.createFile(targetFile);

            // First delete succeeds
            assertTrue(Files.deleteIfExists(targetFile));

            // Second delete returns false (file already gone)
            assertFalse(Files.deleteIfExists(targetFile));
        } finally {
            deleteDirectory(tempDir.toFile());
        }
    }

        of built-in training loops such as `tf.keras` `compile` and `fit`, we
        expect reduction value to be `SUM` or `NONE`. Using `AUTO` in that case
        will raise an error.
    *   `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
        specified by loss function). When this reduction type used with built-in

-a-socialist\u000cis-a-student\u000cis-a-teacher\u000bis-a-techie\u000eis-a-therapist\u0010is-an-accountant\u000bis-an-actor\ris-an-actress\u000fis-an-anarchist\u000cis-an-artist\u000eis-an-engineer\u0011is-an-entertainer\u000cis-certified\u0007is-gone\ris-into-anime\u000cis-into-cars\u0010is-into-cartoons\ris-into-games\u0007is-leet\u0010is-not-certified\u0008is-slick\u000bis-uberleet\u000fis-with-theband\u0008isa-geek\risa-hockeynut\u0010issmarterthanyou\u0009jdevcloud\u0003jpn\u0009kasserver\...

    print("LLM translatable languages:", translatable_langs)
    return translatable_langs


@app.command()
def llm_translatable_json(
    language: Annotated[str | None, typer.Option(envvar="LANGUAGE")] = None,
) -> None:
    translatable_langs = get_llm_translatable()
    if language:
        if language in translatable_langs:
            print(json.dumps([language]))
            return
        else:

   * that returns a {@code ClosingFuture} to its value. The function can use a {@link
   * DeferredCloser} to capture objects to be closed when the pipeline is done (other than those
   * captured by the returned {@link ClosingFuture}).
   *
   * <p>If this {@code ClosingFuture} succeeds, the derived one will be equivalent to the one
   * returned by the function.
   *

### Değiştirerek güncelleme uyarısı { #warning-about-replacing }

Bu, `bar` öğesini `PUT` ile, body içinde şu verilerle güncellemek isterseniz:

```Python
{
    "name": "Barz",
    "price": 3,
    "description": None,
}
```

zaten kayıtlı olan `"tax": 20.2` alanını içermediği için, input model `"tax": 10.5` varsayılan değerini kullanacaktır.

Ve veri, bu "yeni" `tax` değeri olan `10.5` ile kaydedilecektir.

We had this type annotation:

```Python
q: str | None = None
```

What we will do is wrap that with `Annotated`, so it becomes:

```Python
q: Annotated[str | None] = None
```

Both of those versions mean the same thing, `q` is a parameter that can be a `str` or `None`, and by default, it is `None`.

Now let's jump to the fun stuff. 🎉

Response model’inizde şu şekilde default değerler olabilir:

{* ../../docs_src/response_model/tutorial004_py310.py hl[9,11:12] *}

* `description: Union[str, None] = None` (veya Python 3.10’da `str | None = None`) için default `None`’dır.
* `tax: float = 10.5` için default `10.5`’tir.
* `tags: List[str] = []` için default, boş bir list’tir: `[]`.