* @return a new Builder instance
     */
    @Nonnull
    static Builder mvn(@Nonnull List<String> args, @Nonnull MessageBuilderFactory messageBuilderFactory) {
        return builder(Tools.MVN_CMD, Tools.MVN_NAME, args, messageBuilderFactory);
    }

    /**
     * Creates a new Builder instance for constructing a Maven Encrypting Tool ParserRequest.
     *
     * @param args the command-line arguments

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.api;

import javax.tools.DocumentationTool;
import javax.tools.JavaFileManager;
import javax.tools.StandardLocation;

import java.io.File;
import java.nio.file.Path;
import java.util.Objects;
import java.util.Optional;
import java.util.StringJoiner;

your changes by running the command:

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

## Benchmarks und Geschwindigkeit

Wenn Sie sich die Benchmarks ansehen, werden häufig mehrere Tools mit unterschiedlichen Eigenschaften als gleichwertig verglichen.

Konkret geht es darum, Uvicorn, Starlette und FastAPI miteinander zu vergleichen (neben vielen anderen Tools).

But in most of the cases where we need to do something like this, we want the model just to **filter/remove** some of the data as in this example.

And in those cases, we can use classes and inheritance to take advantage of function **type annotations** to get better support in the editor and tools, and still get the FastAPI **data filtering**.

fi

tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS build $TFCI_BAZEL_COMMON_ARGS --config=cuda_wheel //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_BASE_ARGS $TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG --verbose_failures

tfrun "$TFCI_FIND_BIN" ./bazel-bin/tensorflow/tools/pip_package -iname "*.whl" -exec cp {} $TFCI_OUTPUT_DIR \;
tfrun mkdir -p ./dist
tfrun cp $TFCI_OUTPUT_DIR/*.whl ./dist

///

## **FastAPI** Tools

FastAPI stellt für jedes dieser Sicherheitsschemas im Modul `fastapi.security` verschiedene Tools bereit, die die Verwendung dieser Sicherheitsmechanismen vereinfachen.

   *
   * [rfc_7468]: https://tools.ietf.org/html/rfc7468
   */
  fun certificatePem(): String = certificate.certificatePem()

  /**
   * Returns the private key encoded in [PKCS #8][rfc_5208] [PEM format][rfc_7468].
   *
   * [rfc_5208]: https://tools.ietf.org/html/rfc5208
   * [rfc_7468]: https://tools.ietf.org/html/rfc7468
   */
  fun privateKeyPkcs8Pem(): String =

  rm -rf "${tmp_dir}"
}

DIR=$1
mkdir -p "$DIR"
TARBALL_SUFFIX=$2

if [[ $(uname -s) != MSYS_NT* ]]; then
  cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
  cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
  cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"

[pip-compile](https://pypi.org/project/pip-tools/) under the hood):

```
bazel run //ci/official/requirements_updater:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12
```

where `3.12` is the `Python` version you wish to update.

Note, since it is still `pip` and `pip-compile` tools used under the hood, so
most of the command line arguments and features supported by those tools will be