api(libs.conscrypt.openjdk)
  api(libs.openjsse)

  api(libs.junit.jupiter.engine)

  // This runs Corretto on macOS (aarch64) and Linux (x86_64). We don't test Corretto on other
  // operating systems or architectures.
  api(
    variantOf(libs.amazon.corretto) {
      classifier(
        when {
          OperatingSystem.current().isMacOsX -> "osx-aarch_64"
          OperatingSystem.current().isLinux -> "linux-x86_64"

### 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
---- | -------------

	}
	c := p.peek()
	if c == ':' || c == ',' || c == '+' {
		// 2nd register; syntax (R1+R2) etc. No two architectures agree.
		// Check the architectures match the syntax.
		switch p.next().ScanToken {
		case ',':
			if !p.arch.InFamily(sys.ARM, sys.ARM64) {
				p.errorf("(register,register) not supported on this architecture")
				return
			}
		case '+':
			if p.arch.Family != sys.PPC64 {

	// 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 }