labels.q=Anfrage
labels.roles=Rollen
labels.suggestSearchLog=Protokoll mit Suchvorschlägen
labels.suggestWord=Wort vorschlagen
labels.targetLabel=Label
labels.term=Begriff
labels.fields=Felder
labels.ex_q=Erweiterte Query
labels.ldapProviderUrl=LDAP-URL
labels.ldapSecurityPrincipal=Benutzer-DN
labels.ldapAdminSecurityPrincipal=Bind-DN
labels.ldapAdminSecurityCredentials=Passwort

TF_CAPI_EXPORT extern bool TFE_IsCustomDevice(TFE_Context* ctx,
                                              const char* device_name);

// Struct to be filled in to define a custom device tensor handle. Fields are
// required except where indicated.
typedef struct TFE_CustomDeviceTensorHandleMethods {
  int version = TFE_CUSTOM_DEVICE_VERSION;

  // Computes the rank of the tensor handle.
  //

	}

	if err := xlStorage.MakeVol(context.Background(), "dest-vol"); err != nil {
		t.Fatalf("Unable to create volume, %s", err)
	}

	if err := xlStorage.AppendFile(context.Background(), "src-vol", "file1", []byte("Hello, world")); err != nil {
		t.Fatalf("Unable to create file, %s", err)
	}

	if err := xlStorage.AppendFile(context.Background(), "src-vol", "file2", []byte("Hello, world")); err != nil {

import com.google.common.truth.IterableSubject;
import com.google.errorprone.annotations.CanIgnoreReturnValue;
import java.io.Serializable;
import java.lang.reflect.Constructor;
import java.lang.reflect.Field;
import java.lang.reflect.GenericArrayType;
import java.lang.reflect.Method;
import java.lang.reflect.ParameterizedType;
import java.lang.reflect.Type;
import java.lang.reflect.TypeVariable;

  /**
   * Divides an iterator into unmodifiable sublists of the given size (the final list may be
   * smaller). For example, partitioning an iterator containing {@code [a, b, c, d, e]} with a
   * partition size of 3 yields {@code [[a, b, c], [d, e]]} -- an outer iterator containing two
   * inner lists of three and two elements, all in the original order.
   *
   * <p>The returned lists implement {@link java.util.RandomAccess}.
   *

labels.q	=	Reqête
labels.roles	=	Les rôles
labels.suggestSearchLog	=	Suggérer un journal de recherche
labels.suggestWord	=	Suggérer un mot
labels.targetLabel	=	Étiqueter
labels.term	=	Terme
labels.fields	=	Des champs
labels.ex_q	=	Requête étendue
labels.ldapProviderUrl	=	URL LDAP
labels.ldapSecurityPrincipal	=	DN de l'utilisateur
labels.ldapAdminSecurityPrincipal	=	Bind DN

     * the encoded path will have a trailing '/'.
     *
     * Popping "/a/b/c/" yields "/a/b/". In this case the list of path segments goes from ["a",
     * "b", "c", ""] to ["a", "b", ""].
     *
     * Popping "/a/b/c" also yields "/a/b/". The list of path segments goes from ["a", "b", "c"]
     * to ["a", "b", ""].
     */
    private fun pop() {

			accessKey:          credentials.AccessKey,
			secretKey:          credentials.SecretKey,
			expectedRespStatus: http.StatusBadRequest,
		},
		// Test case - 10.
		// Existent bucket with policy with Version field empty.
		// writing BucketPolicy should fail.
		// should result in 400 StatusBadRequest.
		{
			bucketName:         bucketName,

   * sake they are stored interleaved on alternate heap levels in the same array (MMPQ.queue).
   */
  @WeakOuter
  class Heap {
    final Ordering<E> ordering;

    @SuppressWarnings("nullness:initialization.field.uninitialized")
    @Weak
    Heap otherHeap; // always initialized immediately after construction

    Heap(Ordering<E> ordering) {
      this.ordering = ordering;
    }

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.