<offlineLink>
              <url>https://javadoc.io/doc/com.google.j2objc/j2objc-annotations/latest/</url>
              <location>${project.basedir}/javadoc-link/j2objc-annotations</location>
            </offlineLink>
            <offlineLink>
              <url>https://errorprone.info/api/latest/</url>
              <location>${project.basedir}/javadoc-link/error_prone_annotations</location>
            </offlineLink>

Glücklicherweise bietet Pydantic ein großartiges Werkzeug zur Verarbeitung dieser Einstellungen, die von Umgebungsvariablen stammen, mit [Pydantic: Settings Management](https://docs.pydantic.dev/latest/concepts/pydantic_settings/).

### `pydantic-settings` installieren { #install-pydantic-settings }

					return ObjectLocked{}
				}

				if !ret.RetainUntilDate.Before(t) {
					return ObjectLocked{}
				}
				return nil
			}
			// https://docs.aws.amazon.com/AmazonS3/latest/dev/object-lock-overview.html#object-lock-retention-modes
			// If you try to delete objects protected by governance mode and have s3:BypassGovernanceRetention, the operation will succeed.

XML response for this API is similar to [AWS STS AssumeRoleWithWebIdentity](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html#API_AssumeRoleWithWebIdentity_ResponseElements)

### Errors

XML error response for this API is similar to [AWS STS AssumeRoleWithWebIdentity](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html#API_AssumeRoleWithWebIdentity_Errors)

## Sample `POST` Request

TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=1
TFCI_ARTIFACT_LATEST_GCS_URI="gs://tensorflow/versions/latest/"
TFCI_ARTIFACT_STAGING_GCS_ENABLE=1
TFCI_ARTIFACT_STAGING_GCS_URI="gs://tensorflow-ci-staging/staging/versions/${_LOUHI_EXECUTION_ID:-$(git rev-parse HEAD)}/"

# 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
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1

typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.12.0.dev
tb-nightly ~= 2.20.0.a

name: Set nightly branch to master HEAD

permissions: {}

jobs:
  master-to-nightly:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: ubuntu-latest
    permissions:
      contents: write
    steps:
    - uses: zofrex/mirror-branch@0be56f4c8077a288a635a491b306ba0bb1c810e6 # v1.0.4
      name: Set nightly branch to master HEAD
      with:

description: Report a problem with our documentation
labels: [ "a:documentation", "to-triage" ]
assignees: [ ]
body:
  - type: markdown
    attributes:
      value: |
        Please search related information in our [latest documentation](https://docs.gradle.org/current/userguide/userguide.html) before opening a documentation issue.

        <artifactId>rmv</artifactId>
        <version>RELEASE</version>
      </plugin>
      <plugin>
        <groupId>test</groupId>
        <artifactId>lmv</artifactId>
        <version>LATEST</version>
      </plugin>
      <plugin>
        <groupId>test</groupId>
        <artifactId>ifsc</artifactId>
        <version>1/1</version>
      </plugin>
    </plugins>
  </build>