func (v *projP2) FromP1xP1(p *projP1xP1) *projP2 { v.X.Multiply(&p.X, &p.T) v.Y.Multiply(&p.Y, &p.Z) v.Z.Multiply(&p.Z, &p.T) return v } func (v *projP2) FromP3(p *Point) *projP2 { v.X.Set(&p.x) v.Y.Set(&p.y) v.Z.Set(&p.z) return v } func (v *Point) fromP1xP1(p *projP1xP1) *Point { v.x.Multiply(&p.X, &p.T) v.y.Multiply(&p.Y, &p.Z) v.z.Multiply(&p.Z, &p.T) v.t.Multiply(&p.X, &p.Y) return v } func (v *Point) fromP2(p *projP2) *Point { v.x.Multiply(&p.X, &p.Z) v.y.Multiply(&p.Y, &p.Z) v.z.Square(&p.Z)...

* **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 { #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`:

{* ../../docs_src/body_multiple_params/tutorial001_an_py310.py hl[18:20] *}

## Múltiples parámetros del cuerpo

/// note | Nota

Ten en cuenta que, en este caso, el `item` que se tomaría del cuerpo es opcional. Ya que tiene un valor por defecto de `None`.

///

## Múltiples parámetros del cuerpo

En el ejemplo anterior, las *path operations* esperarían un cuerpo JSON con los atributos de un `Item`, como:

```JSON
{

///

## Multiple body parameters { #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`:

///

## 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`:

# Body - Paramètres multiples

Maintenant que nous avons vu comment manipuler `Path` et `Query`, voyons comment faire pour le corps d'une requête, communément désigné par le terme anglais "body".

## Mélanger les paramètres `Path`, `Query` et body

Tout d'abord, sachez que vous pouvez mélanger les déclarations des paramètres `Path`, `Query` et body, **FastAPI** saura quoi faire.