taskNames.any { it.contains("updateReleasedVersions") }
    }


/**
 * If `-DbundleGroovy4=true` is specified, create a distribution using Groovy 4 libs.  Otherwise use Groovy 3 classic libs.
 */
val Project.isBundleGroovy4: Boolean

                this.dependencies.add(dependency);
            }
            return this;
        }

        /**
         * Sets the dependency management to apply to transitive dependencies. To clarify, this management does not
         * apply to
         * the direct dependencies of the root node.
         *
         * @param managedDependencies the dependency management, may be {@code null}

  if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
    return tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
               unwrapped_handle)
        ->DevicePointer();
  }
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.
  if (!tensorflow::TensorHandle::classof(unwrapped_handle)) {

* If you are feeling too generous, you can try to **create an example** like that yourself, just based on the description of the problem. Just keep in mind that this might take a lot of time and it might be better to ask them to clarify the problem first.

### Suggest solutions

* After being able to understand the question, you can give them a possible **answer**.

  public static void rotate(int[] array, int distance, int fromIndex, int toIndex) {
    // There are several well-known algorithms for rotating part of an array (or, equivalently,
    // exchanging two blocks of memory). This classic text by Gries and Mills mentions several:
    // https://ecommons.cornell.edu/bitstream/handle/1813/6292/81-452.pdf.
    // (1) "Reversal", the one we have here.

Ricapitolando, è sufficiente dichiarare **una sola volta** i tipi dei parametri, del body, ecc. come parametri di funzioni.

Questo con le annotazioni per i tipi standard di Python.

Non c'è bisogno di imparare una nuova sintassi, metodi o classi specifici a una libreria, ecc.

È normalissimo **Python 3.6+**.

Per esempio, per un `int`:

```Python
item_id: int
```

o per un modello `Item` più complesso:

```Python

  for (int i = 0; i < *num_handles; ++i) {
    tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =

* 🔧 Update Classiq sponsor links. PR [#4688](https://github.com/tiangolo/fastapi/pull/4688) by [@tiangolo](https://github.com/tiangolo).
* 🔧 Add Classiq sponsor. PR [#4671](https://github.com/tiangolo/fastapi/pull/4671) by [@tiangolo](https://github.com/tiangolo).

   *
   * @since 20.0
   */
  public static final String X_FORWARDED_HOST = "X-Forwarded-Host";

  /**
   * The HTTP <a
   * href="https://docs.aws.amazon.com/elasticloadbalancing/latest/classic/x-forwarded-headers.html#x-forwarded-port">{@code
   * X-Forwarded-Port}</a> header field name.
   *
   * @since 20.0
   */
  public static final String X_FORWARDED_PORT = "X-Forwarded-Port";

feature gate, formerly alpha, is no longer available. Kubernetes now only uses the _structured parameters_ model (also alpha) for allocating dynamic resources to Pods.
  
  if and only if classic DRA was enabled in a cluster, remove all workloads (pods, app deployments, etc. ) which depend on classic DRA and make sure that all PodSchedulingContext resources are gone before upgrading. PodSchedulingContext resources cannot be removed through the apiserver after an upgrade and workloads would not...