### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <dfn title="Technically, Deep Learning model architectures">models</dfn>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

### Container Images

All container images are available as manifest lists and support the described
architectures. It is also possible to pull a specific architecture directly by
adding the "-$ARCH" suffix  to the container image name.

name | architectures
---- | -------------

	// Instruction is a jump.
	IsJump func(word string) bool
}

// nilRegisterNumber is the register number function for architectures
// that do not accept the R(N) notation. It always returns failure.
func nilRegisterNumber(name string, n int16) (int16, bool) {
	return 0, false
}

// Set configures the architecture specified by GOARCH and returns its representation.
// It returns nil if GOARCH is not recognized.

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Astuce

Si vous vous demandez, « AlexNet », « ResNet » et « LeNet » sont juste des noms de <dfn title="Techniquement, architectures de modèles de Deep Learning">modèles</dfn> de Machine Learning.

///

### Déclarer un paramètre de chemin { #declare-a-path-parameter }

## Consequences

* X
* Y
* Z
```

## Consequences

* We start to use Architectural Decision Records
* We use the proposed template from this ADR
* We locate `.md` files in the folder `/architecture/standards`
* We highly encourage usage of ADR to communicate decisions

the precision of the target type, preventing fusion that would discard that rounding.
</p>

<p>
For instance, some architectures provide a "fused multiply and add" (FMA) instruction
that computes <code>x*y + z</code> without rounding the intermediate result <code>x*y</code>.
These examples show when a Go implementation can use that instruction:
</p>