*/
    open fun header(
      name: String,
      value: String,
    ) = commonHeader(name, value)

    /**
     * Adds a header with [name] to [value]. Prefer this method for multiply-valued
     * headers like "Set-Cookie".
     */
    open fun addHeader(
      name: String,
      value: String,
    ) = commonAddHeader(name, value)

    checkBigIntegerConversion("127.255.255.255", BigInteger.valueOf(Integer.MAX_VALUE));
    checkBigIntegerConversion(
        "255.255.255.254", BigInteger.valueOf(Integer.MAX_VALUE).multiply(BigInteger.valueOf(2)));
    checkBigIntegerConversion(
        "255.255.255.255", BigInteger.ONE.shiftLeft(32).subtract(BigInteger.ONE));
  }

  public void testFromIpv6BigIntegerValid() {

      context.get(), components, second_device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  TensorHandlePtr multiply_result(
      Multiply(context.get(), second_combined_value.get(),
               second_negative_one.get(), status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

* **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

		p.firstProg = prog
	} else {
		p.lastProg.Link = prog
	}
	p.lastProg = prog
	if doLabel {
		p.pc++
		for _, label := range p.pendingLabels {
			if p.labels[label] != nil {
				p.errorf("label %q multiply defined", label)
				return
			}
			p.labels[label] = prog
		}
		p.pendingLabels = p.pendingLabels[0:0]
	}
	prog.Pc = p.pc
	if *flags.Debug {
		fmt.Println(p.lineNum, prog)
	}
	if testOut != nil {

			res[i] = &di
		}
	}
	return res
}

// hasSpaceFor returns whether the disks in `di` have space for and object of a given size.
func hasSpaceFor(di []*DiskInfo, size int64) (bool, error) {
	// We multiply the size by 2 to account for erasure coding.
	size *= 2
	if size < 0 {
		// If no size, assume diskAssumeUnknownSize.
		size = diskAssumeUnknownSize
	}

	var available uint64
	var total uint64

			if pctUsed := int(disk.Used * 100 / disk.Total); pctUsed > maxUsedPct {
				maxUsedPct = pctUsed
			}
		}

		// Since we are comparing pools that may have a different number of sets
		// we multiply by the number of sets in the pool.
		// This will compensate for differences in set sizes
		// when choosing destination pool.
		// Different set sizes are already compensated by less disks.
		available *= uint64(nSets[i])

        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)
        result = client.multiply(a, b)`

*   `tf.lite`:

///

## Multiple body parameters

In the previous example, the *path operations* would expect a JSON body with the attributes of an `Item`, like:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

But you can also declare multiple body parameters, e.g. `item` and `user`:

//// tab | Python 3.10+

///

## Múltiplos parâmetros de corpo

No exemplo anterior, as *operações de rota* esperariam um JSON no corpo contendo os atributos de um `Item`, exemplo:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

Mas você pode também declarar múltiplos parâmetros de corpo, por exemplo, `item` e `user`: