It changes the dual-stack API wrt Service from a single ipFamily field to 3
  fields: ipFamilyPolicy (SingleStack, PreferDualStack, RequireDualStack),
  ipFamilies (a list of families assigned), and clusterIPs (inclusive of
  clusterIP).  Most users do not need to set anything at all, defaulting will
  handle it for them.  Services are single-stack unless the user asks for

        *   (since v2.7) `tf.math.segment_mean`
        *   (since v2.7) `tf.math.segment_prod`
        *   (since v2.7) `tf.math.segment_sum`
        *   (since v2.7) `tf.math.unsorted_segment_mean`
        *   (since v2.7) `tf.math.unsorted_segment_prod`
        *   (since v2.7) `tf.math.unsorted_segment_sum`
        *   (since v2.7) `tf.math.unsorted_segment_sqrt`

  After the connection closed(received GOAWAY), the client's other in-flight requests won't be affected, and the client will reconnect. 
  The flag min value is 0 (off), max is .02 (1/50 requests); .001 (1/1000) is a recommended starting point.

      if (newArg == null || Objects.equal(args.get(i), newArg)) {
        if (params.get(i).getType().getRawType().isEnum()) {
          continue; // Nothing better we can do if it's single-value enum
        }
        throw new ParameterHasNoDistinctValueException(params.get(i));
      }
      newArgs.set(i, newArg);
      tester.addEqualityGroup(createInstance(factory, newArgs));

			yBytes := Repeat(y, growLen)
			allocs := testing.AllocsPerRun(100, func() {
				buf.Grow(growLen)
				buf.Write(yBytes)
			})
			// Check no allocation occurs in write, as long as we're single-threaded.
			if allocs != 0 {
				t.Errorf("allocation occurred during write")
			}
			// Check that buffer has correct data.
			if !Equal(buf.Bytes()[0:startLen-readBytes], xBytes[readBytes:]) {

	}

	currentLogger.Trace(tx.Statement.Context, newLogger.BeginAt, func() (string, int64) {
		return newLogger.SQL, tx.RowsAffected
	}, tx.Error)
	tx.Logger = currentLogger
	return
}

// Pluck queries a single column from a model, returning in the slice dest. E.g.:
//
//	var ages []int64
//	db.Model(&users).Pluck("age", &ages)
func (db *DB) Pluck(column string, dest interface{}) (tx *DB) {
	tx = db.getInstance()

    val response = call.execute()
    assertThat(response.body.string()).isEqualTo("ABC")
    assertThat(response.protocol).isEqualTo(protocol)

    // Confirm a single ping was sent and received, and its reply was sent and received.
    val logs = testLogHandler.takeAll()
    assertThat(countFrames(logs, "FINE: >> 0x00000000     8 PING          "))
      .isEqualTo(1)

* **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.

### Concurrency + Parallelism: Web + Machine Learning

   *
   * @since 6.0
   */
  public boolean hasPublicSuffix() {
    return publicSuffixIndex() != NO_SUFFIX_FOUND;
  }

  /**
   * Returns the {@linkplain #isPublicSuffix() public suffix} portion of the domain name, or {@code
   * null} if no public suffix is present.
   *
   * @since 6.0
   */
  @CheckForNull
  public InternetDomainName publicSuffix() {

			t.Errorf("want %q, got %q", testPayload, string(resp))
		}
		t.Log("Roundtrip:", time.Since(start))
	})

	t.Run("localToRemoteErr", func(t *testing.T) {
		const testPayload = "Hello Grid World!"
		start := time.Now()
		resp, err := remoteConn.Request(context.Background(), handlerTest2, []byte(testPayload))
		t.Log("Roundtrip:", time.Since(start))
		if len(resp) != 0 {
			t.Errorf("want nil, got %q", string(resp))
		}