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

        assertBuildFileExists(result, PROJECT_NAME, "build-tools-exported/checkstyle.xml");
        assertBuildFileExists(result, PROJECT_NAME, "build-tools-exported/checkstyle_suppressions.xml");

        result = getGradleRunner().withArguments("buildResources", "-s", "-i").build();
        assertTaskUpToDate(result, ":buildResources");
        assertBuildFileExists(result, PROJECT_NAME, "build-tools-exported/checkstyle.xml");

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server

 generally generated from plugin sources using {{{/plugin-tools/maven-plugin-plugin/}maven-plugin-plugin}}.

* See Also

 * {{{/developers/mojo-api-specification.html}Mojo API Specification}}

 * {{{/plugin-tools/}Plugin Tools}} that provide {{{/plugin-tools/maven-plugin-plugin/}maven-plugin-plugin}} to generate the {{{./plugin.html}<<<META-INF/maven/plugin.xml>>> plugin descriptor}}

This project is published as part of the elasticsearch release and accessible by
`org.elasticsearch.gradle:build-tools:<versionNumber>`.
These build tools are also used by the `elasticsearch-hadoop` project maintained by elastic.

### `build-tools-internal`

This project contains all elasticsearch project specific build logic that is not meant to be shared
with other internal or external projects.

## Build guidelines

<blockquote markdown="1">

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

///

## **FastAPI** utilities { #fastapi-utilities }

FastAPI provides several tools for each of these security schemes in the `fastapi.security` module that simplify using these security mechanisms.

In the next chapters you will see how to add security to your API using those tools provided by **FastAPI**.

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**.

 * 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