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.
  //

  // (`scheme://host:port/path`). Exactly one of `url` or `service`
  // must be specified.
  //
  // The `host` should not refer to a service running in the cluster; use
  // the `service` field instead. The host might be resolved via external
  // DNS in some apiservers (e.g., `kube-apiserver` cannot resolve
  // in-cluster DNS as that would be a layering violation). `host` may
  // also be an IP address.
  //

		}
	}
	if v == "" {
		if !ctx.Args().Present() || ctx.Args().First() == "help" {
			cli.ShowCommandHelpAndExit(ctx, ctx.Command.Name, 1)
		}
		return ctx.Args()
	}
	return strings.Fields(v)
}

func configCommonToSrvCtx(cf config.ServerConfigCommon, ctxt *serverCtxt) {
	ctxt.RootUser = cf.RootUser
	ctxt.RootPwd = cf.RootPwd

	if cf.Addr != "" {
		ctxt.Addr = cf.Addr
	}

    checkEntry(entry1, "custom-bar", "custom-header", 55)
    val entry2 = hpackReader!!.dynamicTable[readerHeaderTableLength() - 2]!!
    checkEntry(entry2, "custom-baz", "custom-header", 55)

    // Once a header field is decoded and added to the reconstructed header
    // list, it cannot be removed from it. Hence, foo is here.
    assertThat(hpackReader!!.getAndResetHeaderList()).isEqualTo(headerBlock)

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;

   * 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.

			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,

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;