}

    @Test
    public void test_getImagePath() {
        Path imagePath = ResourceUtil.getImagePath("logo.png");
        assertNotNull(imagePath);
        assertTrue(imagePath.toString().contains("images"));
    }

    @Test
    public void test_getCssPath() {
        Path cssPath = ResourceUtil.getCssPath("style.css");
        assertNotNull(cssPath);
        assertTrue(cssPath.toString().contains("css"));

- Build/lib/release: Explicitly use '--platform' in building server images
  
  When we switched to go-runner for building the apiserver,
  controller-manager, and scheduler server components, we no longer
  reference the individual architectures in the image names, specifically
  in the 'FROM' directive of the server image Dockerfiles.
  
  As a result, server images for non-amd64 images copy in the go-runner

![Interceptors Diagram](../assets/images/******@****.***)

### Application Interceptors

Interceptors are registered as either _application_ or _network_ interceptors. We'll use the `LoggingInterceptor` defined above to show the difference.

* kubectl rolling-update support for same image ([#24645](https://github.com/kubernetes/kubernetes/pull/24645), [@jlowdermilk](https://github.com/jlowdermilk))
* Add an entry to the salt config to allow Debian jessie on GCE. ([#25123](https://github.com/kubernetes/kubernetes/pull/25123), [@jlewi](https://github.com/jlewi))
    * As with the existing Wheezy image on GCE, docker is expected to already be installed in the image.

					<div class="me-3">
						<a class="link d-none d-sm-flex" href="${doc.url_link}" data-uri="${doc.url_link}" data-id="${doc.doc_id}"
							data-order="${s.index}"
						> <img src="${fe:url('/images/blank.png')}" alt="thumbnail"
							data-src="${fe:url('/thumbnail/')}?docId=${f:u(doc.doc_id)}&queryId=${f:u(queryId)}" class="thumbnail"
						>
						</a>
					</div>
					</c:if>

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

gradlew.bat                             @gradle/bt-developer-productivity
gradle.properties                       @gradle/bt-developer-productivity
gradle/detekt.yml                       @gradle/bt-developer-productivity
.mailmap
images/
.github/ISSUE_TEMPLATE                  @gradle/bt-product-operations
LICENSE                                 @gradle/bt-product-operations

<div align="center">
  <img src="https://www.tensorflow.org/images/tf_logo_horizontal.png">
</div>

[![Python](https://img.shields.io/pypi/pyversions/tensorflow.svg)](https://badge.fury.io/py/tensorflow)
[![PyPI](https://badge.fury.io/py/tensorflow.svg)](https://badge.fury.io/py/tensorflow)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4724125.svg)](https://doi.org/10.5281/zenodo.4724125)

more full featured testing library such as [MockServer](https://www.mock-server.com/).

GraalVM Native Image
--------------------

Building your native images with [GraalVM] should work automatically.

See the okcurl module for an example build.

```shell
$ ./gradlew okcurl:nativeImage
$ ./okcurl/build/graal/okcurl https://httpbin.org/get

          native-image-job-reports: true

      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v5

      - name: Build okcurl
        run: ./gradlew okcurl:nativeBuild

      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v5

      - name: Run native-image tests
        run: ./gradlew -PgraalBuild=true native-image-tests:nativeTest

  android: