"anyOf": [{"type": "string"}, {"type": "null"}],
                            }
                        )
                        | IsDict(
                            # TODO: remove when deprecating Pydantic v1
                            {"title": "Full Name", "type": "string"}
                        ),
                        "disabled": IsDict(
                            {

     */
    String getRepositoryUrl();

    /**
     * The path of the artifact relative to the repository's base URL.
     *
     * @return The path of the artifact, never {@code null}.
     */
    String getName();

    /**
     * Gets the full URL of the artifact.
     *
     * @return The full URL of the artifact, never {@code null}.
     */
    String getUrl();

import static org.junit.jupiter.api.Assertions.assertEquals;

/**
 */
class ThreadOutputMuxerTest {

    final String paid = "Paid";

    final String in = "In";

    final String full = "Full";

    @Test
    void testSingleThreaded() throws Exception {
        ProjectBuildList src = getProjectBuildList();

    email="******@****.***",
    full_name=None,
)
```

Или, если для большей точности мы напрямую используем `user_dict` с любым потенциальным содержимым, то этот пример будет выглядеть так:

```Python
UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

     */
    public static String getFullPattern() {
        return getFullPattern(LocaleUtil.getDefault());
    }

    /**
     * 指定されたロケールで{@link DateFormat#FULL}スタイルのパターン文字列を返します。
     *
     * @param locale
     *            ロケール。{@literal null}であってはいけません
     * @return {@link DateFormat#FULL}スタイルのパターン文字列
     */
    public static String getFullPattern(final Locale locale) {

from pydantic import BaseModel

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret2",

from typing_extensions import Annotated

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret2",

from pydantic import BaseModel

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret2",

Using it led to the creation of several Flask full-stack generators. These are the main stacks I (and several external teams) have been using up to now:

* <a href="https://github.com/tiangolo/full-stack" class="external-link" target="_blank">https://github.com/tiangolo/full-stack</a>
* <a href="https://github.com/tiangolo/full-stack-flask-couchbase" class="external-link" target="_blank">https://github.com/tiangolo/full-stack-flask-couchbase</a>

    password="secret",
    email="******@****.***",
    full_name=None,
)
```

Or more exactly, using `user_dict` directly, with whatever contents it might have in the future:

```Python
UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```