This has been mostly done, but there remain a few holdouts (fixing these is out of scope).

To keep the Groovy DSL ergonomic, we generate methods as necessary from the non-Groovy equivalents.

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.

#### Optimizations

A naive implementation would simply regenerate all resources, of all subscribed types, for each client, on any configuration change. However, this scales poorly. As a result, we have many levels of optimizations to avoid doing this work.

/**
 * Extracts Kotlin DSL runtime generated sources.
 *
 * Current implementation extracts these from the wrapper's API jars.
 * This is not correct as it should do this with the built distribution instead.
 *
 * Doing it correctly would require running a Gradle build with the full
 * distribution and extracting the generated api jar from its Gradle user home,
 * slowing down building documentation.
 *

		"-j", "CONNMARK",
		"--set-xmark", inpodTproxyMark)

	// Handle healthcheck probes from the host node. In the host netns, before the packet enters the pod, we SNAT
	// the healthcheck packet to a fixed IP if the packet is coming from a node-local process with a socket.
	//
	// We do this so we can exempt this traffic from ztunnel capture/proxy - otherwise both kube-proxy (legit)

      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {
    // We must test on a fresh clone of the dataset each time. Doing sorts and quickselects on a
    // dataset which is already sorted or partially sorted is cheating.
    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;

      <version>4.13.2</version>
    </dependency>
    <dependency>
      <!--
      Do not include Truth in non-test scope! Doing so creates a problematic dependency cycle.
      -->
      <groupId>com.google.truth</groupId>
      <artifactId>truth</artifactId>
      <version>${truth.version}</version>
      <scope>test</scope>
      <exclusions>

import org.junit.jupiter.api.Tag
import org.junit.jupiter.api.Test

/**
 * Integration test to confirm that [TaskRunner] works with a real backend. Business logic is all
 * exercised by [TaskRunnerTest].
 *
 * This test is doing real sleeping with tolerances of 250 ms. Hopefully that's enough for even the
 * busiest of CI servers.
 */
@Tag("Slowish")
class TaskRunnerRealBackendTest {
  private val log = LinkedBlockingDeque<String>()

        or even industries — utilities useful for a sizable proportion of all Java programmers
        everywhere. If you can give enough detail such that any of us can imagine coming across
        a similar need in our own work, that's extremely helpful in studying how broadly useful the
        feature will be.

  - type: textarea
    attributes:
      label: Concrete Use Cases

      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {
    // We must test on a fresh clone of the dataset each time. Doing sorts and quickselects on a
    // dataset which is already sorted or partially sorted is cheating.
    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;

* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc.
* **Convert data**: from the request to the required type.
* **Validate data**: coming from each request:
    * Generating **automatic errors** returned to the client when the data is invalid.
* **Document** the API using OpenAPI:
    * which is then used by the automatic interactive documentation user interfaces.