within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training...

                    val versionsOfMinor = entry.value.sortedByDescending { VersionNumber.parse(it) }
                    if (idx < versionsByMinor.size - 1) {
                        // Latest of the previous minor
                        add(versionsOfMinor.first())
                    } else {
                        // Current minor
                        val versionsByPatch = versionsOfMinor

        assertEquals(0, SMBUtil.readInt4(buffer, bodyOffset + 8));

        // Verify token length
        assertEquals(TEST_TOKEN.length, SMBUtil.readInt2(buffer, bodyOffset + 14));

        // Verify previous session ID
        assertEquals(TEST_PREVIOUS_SESSION_ID, SMBUtil.readInt8(buffer, bodyOffset + 16));

        // Verify token content at the security buffer offset

        @Override
        public Integer next(Integer value) {
          return integers().next(value);
        }

        @Override
        public Integer previous(Integer value) {
          return integers().previous(value);
        }

        @Override
        public long distance(Integer start, Integer end) {
          return integers().distance(start, end);
        }

        @Override

# Get Current User { #get-current-user }

In the previous chapter the security system (which is based on the dependency injection system) was giving the *path operation function* a `token` as a `str`:

{* ../../docs_src/security/tutorial001_an_py39.py hl[12] *}

But that is still not that useful.

Let's make it give us the current user.

## Create a user model { #create-a-user-model }

First, let's create a Pydantic user model.

    okayToRun.countDown();
    thread1.join();
    thread2.join();
    assertEquals(1, runCalled.get());
  }

  public void testAddAfterRun() throws Exception {
    // Run the previous test
    testRunOnPopulatedList();

    // If it passed, then verify an Add will be executed without calling run
    CountDownLatch countDownLatch = new CountDownLatch(1);

	"github.com/minio/minio/internal/kms"
)

const (
	minioConfigPrefix = "config"
	minioConfigBucket = minioMetaBucket + SlashSeparator + minioConfigPrefix
	kvPrefix          = ".kv"

	// Captures all the previous SetKV operations and allows rollback.
	minioConfigHistoryPrefix = minioConfigPrefix + "/history"

	// MinIO configuration file.
	minioConfigFile = "config.json"
)

         * @return the total page count
         */
        public int getAllPageCount() {
            return allPageCount;
        }

        /**
         * Checks if a previous page exists.
         *
         * @return true if there is a previous page, false otherwise
         */
        public boolean isExistPrePage() {
            return currentPageNumber != 1;
        }

        /**

* Getting Logger Instance

 Starting with Maven 3.1.0, SLF4J Logger can be used directly. This technique can be used safely in Maven core
 components or in plugins/component not requiring compatibility with previous Maven versions.

+-----+
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class MyClass
{
   final Logger logger = LoggerFactory.getLogger( MyClass.class );
}
+-----+

         * processes one artifact at a time and hence cannot associate the artifacts from the same project to use the
         * same version index. Allowing the caller to pass in metadata from a previous deployment allows to re-establish
         * the association between the artifacts of the same project.
         */
        for (Iterator<? extends Metadata> it = metadatas.iterator(); it.hasNext(); ) {