build:win_clang --extra_execution_platforms=//tensorflow/tools/toolchains/win:x64_windows-clang-cl
build:win_clang --host_platform=//tensorflow/tools/toolchains/win:x64_windows-clang-cl

build:windows_x86_cpu_2022 --config=win_clang_base
build:windows_x86_cpu_2022 --crosstool_top="//tensorflow/tools/toolchains/win2022/20241118:toolchain"
build:windows_x86_cpu_2022 --extra_toolchains="//tensorflow/tools/toolchains/win2022/20241118:cc-toolchain-x64_windows-clang-cl"

 * {@code *} character has no special meaning as part of a parameter. All values for type, subtype,
 * parameter attributes or parameter values must be valid according to RFCs <a
 * href="https://tools.ietf.org/html/rfc2045">2045</a> and <a
 * href="https://tools.ietf.org/html/rfc2046">2046</a>.
 *
 * <p>All portions of the media type that are case-insensitive (type, subtype, parameter attributes)

}

@test "API compatibility test passes, ensuring no unexpected changes to the TF API" {
    bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test

**FastAPI** würde ohne die frühere Arbeit anderer nicht existieren.

Es wurden zuvor viele Tools entwickelt, die als Inspiration für seine Entwicklung dienten.

Ich habe die Schaffung eines neuen Frameworks viele Jahre lang vermieden. Zuerst habe ich versucht, alle von **FastAPI** abgedeckten Funktionen mithilfe vieler verschiedener Frameworks, Plugins und Tools zu lösen.

## 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.

 *   <li>Master password management</li>
 *   <li>Security settings configuration</li>
 * </ul>
 *
 * @see org.apache.maven.api.cli.Tools#MVNENC_CMD
 * @see org.apache.maven.api.cli.Tools#MVNENC_NAME
 * @since 4.0.0
 */

 *   <li>Dependency version management</li>
 *   <li>Configuration migration</li>
 *   <li>Interactive upgrade workflows</li>
 * </ul>
 *
 * @see org.apache.maven.api.cli.Tools#MVNUP_CMD
 * @see org.apache.maven.api.cli.Tools#MVNUP_NAME
 * @since 4.0.0
 */

/xla_configure.bazelrc
/bazel-*
/bazel_pip
/tools/python_bin_path.sh
/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**

     *
     * @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

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**.