firSymbolBuilder.typeBuilder.buildKtType(coneKotlinType)
            },
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirJsErrors.WRONG_MULTIPLE_INHERITANCE) { firDiagnostic ->
        WrongMultipleInheritanceImpl(
            firSymbolBuilder.callableBuilder.buildCallableSymbol(firDiagnostic.a),
            firDiagnostic as KtPsiDiagnostic,
            token,

  private val tasksExecutor = Executors.newCachedThreadPool(threadFactory("TaskFaker"))

  /**
   * True if this task faker has ever had multiple tasks scheduled to run concurrently. Guarded by
   * [TaskRunner.lock].
   */
  var isParallel = false

  /** Number of calls to [TaskRunner.Backend.execute]. Guarded by [TaskRunner.lock]. */
  var executeCallCount = 0

  fun maxDataLength(): Int = maxFrameSize

  /**
   * `source.length` may be longer than the max length of the variant's data frame. Implementations
   * must send multiple frames as necessary.
   *
   * @param source the buffer to draw bytes from. May be null if byteCount is 0.
   * @param byteCount must be between 0 and the minimum of `source.length` and [maxDataLength].
   */

All source code in Gradle is written to target one or more of these runtimes.
Most source code targets the daemon and the remaining code either targets a single runtime, for example the Gradle client, or is shared across multiple runtimes.

## Composition by architecture modules

Each [architecture module and platform](platforms.md) can contribute code to any of the runtimes.
Not every module contributes to every runtime.

    id("${model.projectId}_FlakyQuarantine_${os.asName()}_${arch.asName()}")
    name = "Flaky Test Quarantine - ${os.asName()} ${arch.asName()}"
    description = "Run all flaky tests skipped multiple times"

    applyDefaultSettings(os = os, arch = arch, buildJvm = BuildToolBuildJvm, timeout = 180)

    val testsWithOs = model.stages.filter {
        it.stageName in listOf(

* **Easy**: Designed to be easy to use and learn. Less time reading docs.
* **Short**: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.
* **Robust**: Get production-ready code. With automatic interactive documentation.

The webhooks that you define will end up in the **OpenAPI** schema and the automatic **docs UI**.

!!! info
    The `app.webhooks` object is actually just an `APIRouter`, the same type you would use when structuring your app with multiple files.

* This also allows using automatic **client code generation** in many languages.

### Automatic docs

Interactive API documentation and exploration web user interfaces. As the framework is based on OpenAPI, there are multiple options, 2 included by default.

        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)
        result = client.multiply(a, b)`

*   `tf.lite`:

    }

  /**
   * The maximum number of requests for each host to execute concurrently. This limits requests by
   * the URL's host name. Note that concurrent requests to a single IP address may still exceed this
   * limit: multiple hostnames may share an IP address or be routed through the same HTTP proxy.
   *
   * If more than [maxRequestsPerHost] requests are in flight when this is invoked, those requests
   * will remain in flight.
   *