* acquire(1000)} will result in exactly the same throttling, if any), but it affects the throttling
 * of the <i>next</i> request. I.e., if an expensive task arrives at an idle RateLimiter, it will be
 * granted immediately, but it is the <i>next</i> request that will experience extra throttling,
 * thus paying for the cost of the expensive task.
 *

    update your imports accordingly, the old files will be removed in Release
    2.11.
*   `tf.keras.optimizers.experimental.Optimizer` will graduate in Release 2.11,
    which means `tf.keras.optimizers.Optimizer` will be an alias of
    `tf.keras.optimizers.experimental.Optimizer`. The current
    `tf.keras.optimizers.Optimizer` will continue to be supported as
    `tf.keras.optimizers.legacy.Optimizer`,

"elég", "ellen", "elő", "először", "előtt", "első", "én", "éppen", "ebben", "ehhez", "emilyen", "ennek", "erre", "ez", "ezt", "ezek", "ezen", "ezzel", "ezért", "és", "fel", "felé", "hanem", "hiszen", "hogy", "hogyan", "igen", "így", "illetve", "ill.", "ill", "ilyen", "ilyenkor", "ison", "ismét", "itt", "jó", "jól", "jobban", "kell", "kellett", "keresztül", "keressünk", "ki", "kívül", "között", "közül", "legalább", "lehet", "lehetett", "legyen", "lenne", "lenni", "lesz", "lett", "maga", "magát", "majd",...

"elég", "ellen", "elő", "először", "előtt", "első", "én", "éppen", "ebben", "ehhez", "emilyen", "ennek", "erre", "ez", "ezt", "ezek", "ezen", "ezzel", "ezért", "és", "fel", "felé", "hanem", "hiszen", "hogy", "hogyan", "igen", "így", "illetve", "ill.", "ill", "ilyen", "ilyenkor", "ison", "ismét", "itt", "jó", "jól", "jobban", "kell", "kellett", "keresztül", "keressünk", "ki", "kívül", "között", "közül", "legalább", "lehet", "lehetett", "legyen", "lenne", "lenni", "lesz", "lett", "maga", "magát", "majd",...

            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

...but, you can have separated models using Pydantic v1 and v2 in the same app.

```mermaid
graph TB
    subgraph "✅ Supported"
        direction TB

 * whenever there is a chance the check may fail "in real life". Example:
 *
 * {@snippet :
 * Bill bill = remoteService.getLastUnpaidBill();
 *
 * // In case bug 12345 happens again we'd rather just die
 * Verify.verify(bill.status() == Status.UNPAID,
 *     "Unexpected bill status: %s", bill.status());
 * }
 *
 * <h3>Comparison to alternatives</h3>
 *

numpy>=2.2.6 ; python_version == "3.14"
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0 ; python_version <= "3.13"
h5py ~= 3.15.1 ; python_version == "3.14"
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.4, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

**Note**: You can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.

Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.

But by following the steps above, it will be able to do some performance optimizations.

## Technical Details { #technical-details }

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

      Iterable<E> expectedElements,
      KnownOrder knownOrder,
      int startIndex) {
    // periodically we should manually try (steps * 3 / 2) here; all tests but
    // one should still pass (testVerifyGetsCalled()).
    stimuli = (Stimulus<E, ? super I>[]) new Stimulus<?, ?>[steps];
    checkArgument(elementsToInsertIterable.iterator().hasNext());
    elementsToInsert = Helpers.cycle(elementsToInsertIterable);