Here's an example of how an HTTPS API could look like, step by step, paying attention mainly to the ideas important for developers.

### Domain Name { #domain-name }

It would probably all start by you **acquiring** some **domain name**. Then, you would configure it in a DNS server (possibly your same cloud provider).

        // Act & Assert
        // unwrap to interface itself should return same instance
        CredentialsInternal unwrapped = creds.unwrap(CredentialsInternal.class);
        assertNotNull(unwrapped, "unwrap to CredentialsInternal returns instance");
        assertSame(creds, unwrapped, "unwrap returns the same object instance");

	encodedQuery := query.Encode()

	// Verify if date query is same.
	if req.Form.Get(xhttp.AmzDate) != query.Get(xhttp.AmzDate) {
		return ErrSignatureDoesNotMatch
	}
	// Verify if expires query is same.
	if req.Form.Get(xhttp.AmzExpires) != query.Get(xhttp.AmzExpires) {
		return ErrSignatureDoesNotMatch
	}
	// Verify if signed headers query is same.

	"github.com/secure-io/sio-go"
	"github.com/secure-io/sio-go/sioutil"
)

// EncryptBytes encrypts the plaintext with a key managed by KMS.
// The context is bound to the returned ciphertext.
//
// The same context must be provided when decrypting the
// ciphertext.
func EncryptBytes(k *kms.KMS, plaintext []byte, context kms.Context) ([]byte, error) {
	ciphertext, err := Encrypt(k, bytes.NewReader(plaintext), context)

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

# Extra Data Types { #extra-data-types }

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    steps:
    - name: Checkout code
      uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0

   *       (<i>symmetric</i> property)
   *   <li>If {@code equivalent(x, y)} and {@code equivalent(y, z)} are both true, then {@code
   *       equivalent(x, z)} is also true (<i>transitive</i> property)
   * </ul>
   *
   * <p>Note that all calls to {@code equivalent(x, y)} are expected to return the same result as
   * long as neither {@code x} nor {@code y} is modified.

They are just a convenience, they hold the same number, but that way you can use the editor's autocomplete to find them:

<img src="/img/tutorial/response-status-code/image02.png">

/// note | Technical Details

You could also use `from starlette import status`.

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).