* in the Gradle build.
 *
 * Each project may require a set of "target runtimes" that it must be able to run on.
 * Each target runtime reflects a runtime environment where code is executed. For one
 * project to be able to run in a given runtime, all of its dependencies must also
 * support running in that runtime.
 *

</div>

/// tip

With `pwdlib`, you could even configure it to be able to read passwords created by **Django**, a **Flask** security plug-in or many others.

So, you would be able to, for example, share the same data from a Django application in a database with a FastAPI application. Or gradually migrate a Django application using the same database.

And your users would be able to login from your Django app or from your **FastAPI** app, at the same time.

    tags = [
        "no_cuda_asan",  # TODO(b/181771536)
        "no_windows",  # TODO(b/155444728)
    ],
    # We must ensure that the dependencies can be dynamically linked since
    # the shared library must be able to use core:framework.
    deps = [
        ":c_api",
        ":c_api_internal",
        ":c_test_util",
        ":test_op_kernel",
        ":tf_buffer",
        ":tf_buffer_internal",

	if err != nil {
		t.Fatalf("Unable to initialize new http request %s", err)
	}
	return req
}

// This is similar to mustNewRequest but additionally the request
// is signed with AWS Signature V4, fails if not able to do so.
func mustNewSignedRequest(method string, urlStr string, contentLength int64, body io.ReadSeeker, t *testing.T) *http.Request {
	req := mustNewRequest(method, urlStr, contentLength, body, t)
	cred := globalActiveCred

```Dockerfile
RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
```

The file with the package requirements **won't change frequently**. So, by copying only that file, Docker will be able to **use the cache** for that step.

And then, Docker will be able to **use the cache for the next step** that downloads and install those dependencies. And here's where we **save a lot of time**. ✨ ...and avoid boredom waiting. 😪😆

* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

We'll see how they would affect **deployments**.

In the end, the ultimate objective is to be able to **serve your API clients** in a way that is **secure**, to **avoid disruptions**, and to use the **compute resources** (for example remote servers/virtual machines) as efficiently as possible. 🚀

```Python
from .routers.items import router
from .routers.users import router
```

the `router` from `users` would overwrite the one from `items` and we wouldn't be able to use them at the same time.

So, to be able to use both of them in the same file, we import the submodules directly:

{* ../../docs_src/bigger_applications/app_an_py310/main.py hl[5] title["app/main.py"] *}

        // Success should reset counter
        assertTrue(rateLimiter.checkAttempt(username, ip));
        rateLimiter.recordSuccess(username, ip);

        // Should be able to fail 3 more times before lockout
        for (int i = 0; i < 3; i++) {
            assertTrue(rateLimiter.checkAttempt(username, ip));
            rateLimiter.recordFailure(username, ip);
        }

## Dependencies with `yield` and `except` { #dependencies-with-yield-and-except }

If you catch an exception using `except` in a dependency with `yield` and you don't raise it again (or raise a new exception), FastAPI won't be able to notice there was an exception, the same way that would happen with regular Python:

{* ../../docs_src/dependencies/tutorial008c_an_py310.py hl[15:16] *}

      } finally {
        monitor.leave();
      }
    } else {
      // It is possible due to races that we are currently in the expected state even though we
      // timed out. e.g. if we weren't event able to grab the lock within the timeout we would never
      // even check the guard. I don't think we care too much about this use case but it could lead
      // to a confusing error message.