memAvailableMD = NewGaugeMD(memAvailable, "Available memory on the node")
)

// loadMemoryMetrics - `MetricsLoaderFn` for node memory metrics.
func loadMemoryMetrics(ctx context.Context, m MetricValues, c *metricsCache) error {
	memMetrics, err := c.memoryMetrics.Get()
	if err != nil {
		metricsLogIf(ctx, err)
		return err
	}

	m.Set(memTotal, float64(memMetrics.Total))
	m.Set(memUsed, float64(memMetrics.Used))

  val size: Int
    get() = encodedNames.size

  @JvmName("-deprecated_size")
  @Deprecated(
    message = "moved to val",
    replaceWith = ReplaceWith(expression = "size"),
    level = DeprecationLevel.ERROR,
  )
  fun size(): Int = size

  fun encodedName(index: Int): String = encodedNames[index]

  fun name(index: Int): String = encodedName(index).percentDecode(plusIsSpace = true)

     */
    @Nonnull
    List<? extends ModelBuilderResult> getChildren();

    /**
     * Creates a human-readable representation of these errors.
     */
    @Override
    String toString();

            final String content = response.getContentAsString();
            logger.info(content);
            assertTrue(content.length() > 0);
        }, e -> {
            logger.log(Level.SEVERE, "error", e);
            fail();
        });
    }

    @Test
    public void test_GetFactoryMethod() {
        // ## Arrange ##
        final String url = "http://example.com";

        // ## Act ##

Daher erstellen wir eine zusätzliche Abhängigkeit `get_current_active_user`, die wiederum `get_current_user` als Abhängigkeit verwendet.

Beide Abhängigkeiten geben nur dann einen HTTP-Error zurück, wenn der Benutzer nicht existiert oder inaktiv ist.

In unserem Endpunkt erhalten wir also nur dann einen Benutzer, wenn der Benutzer existiert, korrekt authentifiziert wurde und aktiv ist:

                    .forEach(path -> {
                        try {
                            Files.delete(path);
                        } catch (IOException e) {
                            // Ignore cleanup errors
                        }
                    });
        }
    }

    @Test
    public void testRequestDurableHandle() {

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

app = SomeASGIApp()

new_app = UnicornMiddleware(app, some_config="rainbow")
```

But FastAPI (actually Starlette) provides a simpler way to do it that makes sure that the internal middlewares handle server errors and custom exception handlers work properly.

For that, you use `app.add_middleware()` (as in the example for CORS).

```Python
from fastapi import FastAPI
from unicorn import UnicornMiddleware

app = FastAPI()

      render: shell
    validations:
      required: true
  - type: textarea
    id: what-did-you-do
    attributes:
      label: "What did you do?"
      description: "If possible, provide a recipe for reproducing the error. A complete runnable program is good. A link on [go.dev/play](https://go.dev/play) is better. A failing unit test is the best."
    validations:
      required: true
  - type: textarea
    id: actual-behavior
    attributes:

)

// loadClusterNotificationMetrics - `MetricsLoaderFn` for cluster notification metrics.
func loadClusterNotificationMetrics(_ context.Context, m MetricValues, _ *metricsCache) error {
	if globalEventNotifier == nil {
		return nil
	}

	nstats := globalEventNotifier.targetList.Stats()
	m.Set(notificationCurrentSendInProgress, float64(nstats.CurrentSendCalls))