## Previous tools { #previous-tools }

### <a href="https://www.djangoproject.com/" class="external-link" target="_blank">Django</a> { #django }

It's the most popular Python framework and is widely trusted. It is used to build systems like Instagram.

     *
     * @return the toolchain model
     */
    ToolchainModel getModel();

    /**
     * Gets the platform tool executable.
     *
     * @param toolName the tool platform independent tool name
     * @return file representing the tool executable, or null if the tool cannot be found
     */
    String findTool(String toolName);

    /**
     * Let the toolchain decide if it matches requirements defined

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-utilities }

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

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

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**.

## Vorherige Tools { #previous-tools }

### <a href="https://www.djangoproject.com/" class="external-link" target="_blank">Django</a> { #django }

  <description>Test the use of a system scoped dependency to a (fake) tools.jar.</description>

  <dependencies>
    <dependency>
      <groupId>com.sun</groupId>
      <artifactId>tools</artifactId>
      <version>1.4.2</version>
      <scope>system</scope>
      <systemPath>${jre.home}/../lib/tools.jar</systemPath>
    </dependency>
  </dependencies>

  <build>
    <plugins>
      <plugin>

  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