}
	}

	setCounts = []uint64{}
	for setCount := range newSetCounts {
		setCounts = append(setCounts, setCount)
	}

	// Not necessarily needed but it ensures to the readers
	// eyes that we prefer a sorted setCount slice for the
	// subsequent function to figure out the right common
	// divisor, it avoids loops.
	sort.Slice(setCounts, func(i, j int) bool {
		return setCounts[i] < setCounts[j]
	})

The `string()` method on response body is convenient and efficient for small documents. But if the response body is large (greater than 1 MiB), avoid `string()` because it will load the entire document into memory. In that case, prefer to process the body as a stream.

=== ":material-language-kotlin: Kotlin"
    ```kotlin
      private val client = OkHttpClient()

      fun run() {
        val request = Request.Builder()

**replicate the containers** in the **available machines** taking into account the amount of memory needed by them, and the amount available in the machines in the cluster.

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs...

Maar stel je voor dat je weer bezig bent met het maken van een functie, maar deze keer met type hints.

Op hetzelfde moment probeer je de automatische aanvulling te activeren met `Ctrl+Spatie` en je ziet:

<img src="/img/python-types/image02.png">

		},
		// Test case - 10.
		// Case with proper components
		{
			bucketName: bucketName,
			objectName: "etc/path/proper/.../etc",
			byteRange:  "",
			accessKey:  credentials.AccessKey,
			secretKey:  credentials.SecretKey,

			expectedContent: encodeResponse(getAPIErrorResponse(ctx,
				getAPIError(ErrNoSuchKey),
				getGetObjectURL("", bucketName, "etc/path/proper/.../etc"),
				"", "")),

```Python hl_lines="1  5-7" title="app/dependencies.py"
{!> ../../docs_src/bigger_applications/app_an/dependencies.py!}
```

////

//// tab | Python 3.8+ non-Annotated

/// tip

Prefer to use the `Annotated` version if possible.

///

```Python hl_lines="1  4-6" title="app/dependencies.py"
{!> ../../docs_src/bigger_applications/app/dependencies.py!}
```

////

/// tip

{!> ../../docs_src/response_model/tutorial002.py!}
```

////

Now, whenever a browser is creating a user with a password, the API will return the same password in the response.

In this case, it might not be a problem, because it's the same user sending the password.

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

/// danger

 *       running on it. This class, by contrast, does not expose an {@code Executor} API.)
 * </ul>
 *
 * <p>If you don't need the features of this class, you may prefer {@code newSequentialExecutor} for
 * its simplicity and ability to accommodate interruption.
 *
 * @since 26.0
 */
@ElementTypesAreNonnullByDefault
@J2ktIncompatible
public final class ExecutionSequencer {

have both been migrated.

### gRPC Probes Graduate to Beta

With Kubernetes 1.24, the [gRPC probes functionality](https://github.com/kubernetes/enhancements/issues/2727)
has entered beta and is available by default. You can now [configure startup, liveness, and readiness probes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#configure-probes) for your gRPC app

   * We get plan2 as a follow-up to plan1, typically retry the same IP but different TLS.
   * We get plan3 as a retry of plan0, which was canceled when it lost the race.
   *
   * This test confirms that we prefer to do the TLS follow-up (plan2) before the TCP retry (plan3).
   * It also confirms we enforce the 250 ms delay in each race.
   */
  @Test
  fun tcpConnectionsRaceAfterTlsFails() {
    val plan0 = routePlanner.addPlan()