* MD5 (128 hash bits) hash function and the given secret key.
   *
   * <p>If you are designing a new system that needs HMAC, prefer {@link #hmacSha256} or other
   * future-proof algorithms <a
   * href="https://datatracker.ietf.org/doc/html/rfc6151#section-2.3">over {@code hmacMd5}</a>.
   *
   * @param key the secret key

A strong MinIO PR:
- Delivers a complete, valuable change (feature, bug fix, or improvement).
- Has a concise title (e.g., `[S3] Fix bucket policy parsing #1234`) and a summary with context, referencing issues (e.g., `#1234`).
- Contains well-written, logical commits explaining *why* changes were made (e.g., “Add S3 bucket tagging support so that users can organize resources efficiently”).

Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

und dann aufrufen:

```Python
user_dict = user_in.model_dump()
```

У Pydantic-моделей есть метод `.dict()`, который возвращает `dict` с данными модели.

Поэтому, если мы создадим Pydantic-объект `user_in` таким способом:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

и затем вызовем:

```Python
user_dict = user_in.dict()
```

    interface EnumConsumer {
        void accept(RequestParam rp);
    }

    @Mock
    EnumConsumer consumer;

    @Test
    @DisplayName("Enum value can be passed to collaborators without side effects")
    void enumUsedWithCollaboratorHasNoSideEffects() {
        // Arrange: pick a value and a mocked consumer

- admin:CreateServiceAccount
- admin:UpdateServiceAccount
- admin:RemoveServiceAccount
- admin:ListServiceAccounts

#### Bucket quota management permissions

- admin:SetBucketQuota
- admin:GetBucketQuota

#### Bucket target management permissions

- admin:SetBucketTarget
- admin:GetBucketTarget

#### Remote tier management permissions

- admin:SetTier

    ): List<SmallSubprojectBucket> {
        // splitIntoBuckets() method expects us to split large element into N elements,
        // but we want to have a single bucket with N batches.
        // As a workaround, we repeat the bucket N times, and deduplicate the result at the end
        val resultIncludingDuplicates =
            splitIntoBuckets(
                LinkedList(subProjectTestClassTimes),

# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/ml_build_arm64"
TFCI_INDEX_HTML_ENABLE=1

import org.opensearch.search.aggregations.bucket.filter.FilterAggregationBuilder;
import org.opensearch.search.aggregations.bucket.global.GlobalAggregationBuilder;
import org.opensearch.search.aggregations.bucket.histogram.HistogramAggregationBuilder;
import org.opensearch.search.aggregations.bucket.missing.MissingAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;

  # Run this after labeler applied labels
  check-labels:
    needs:
      - labeler
    permissions:
      pull-requests: read
    runs-on: ubuntu-latest
    steps:
      - uses: docker://agilepathway/pull-request-label-checker:latest
        with:
          one_of: breaking,security,feature,bug,refactor,upgrade,docs,lang-all,internal