model is called for the first time.
*   `tf.linalg`:
    *   Add `CompositeTensor` as a base class to `LinearOperator`.
*   `tf.lite`:
    *   Fix mean op reference quantization rounding issue.
    *   Added `framework_stable` BUILD target, which links in only the
        non-experimental TF Lite APIs.
    *   Remove deprecated Java `Interpreter` methods:

# These are supported funding model platforms

github: [jinzhu]
patreon: jinzhu

    is_body_allowed_for_status_code,
)
from pydantic import BaseModel
from starlette import routing
from starlette.concurrency import run_in_threadpool
from starlette.exceptions import HTTPException
from starlette.requests import Request
from starlette.responses import JSONResponse, Response
from starlette.routing import (
    BaseRoute,
    Match,
    compile_path,
    get_name,
    request_response,

good as it can be within reason.\n * Discussion here: https://github.com/FezVrasta/popper.js/pull/715\n *\n * Low DPI screens cause a popper to be blurry if not using full pixels (Safari\n * as well on High DPI screens).\n *\n * Firefox prefers no rounding for positioning and does not have blurriness on\n * high DPI screens.\n *\n * Only horizontal placement and left/right values need to be considered.\n */\nexport default function getRoundedOffsets(data, shouldRound) {\n  const { popper, reference...

Sebastián Ramírez <******@****.***> 1589724233 +0200

- Adds feature gate `KubeletInUserNamespace` which enables support for running kubelet in a user namespace.
  
  The user namespace has to be created before running kubelet.
  All the node components such as CRI need to be running in the same user namespace.
  

    private final CountDownLatch stopLatch = new CountDownLatch(1);

    private volatile boolean running = false;

    @Override
    public @Nullable Void call() throws InterruptedException {
      running = true;
      startLatch.countDown();
      stopLatch.await();
      running = false;
      return null;
    }

    public void waitForStart() throws InterruptedException {

If you need to have two independent FastAPI applications, with their own independent OpenAPI and their own docs UIs, you can have a main app and "mount" one (or more) sub-application(s).

## Mounting a **FastAPI** application

"Mounting" means adding a completely "independent" application in a specific path, that then takes care of handling everything under that path, with the _path operations_ declared in that sub-application.

# Benchmarks

Independent TechEmpower benchmarks show **FastAPI** applications running under Uvicorn as <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">one of the fastest Python frameworks available</a>, only below Starlette and Uvicorn themselves (used internally by FastAPI).

### Providing the `root_path`

To achieve this, you can use the command line option `--root-path` like:

<div class="termy">

```console
$ fastapi run main.py --root-path /api/v1

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

</div>