Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

//// tab | Pydantic v2

import org.apache.maven.artifact.resolver.ArtifactResolutionException;
import org.apache.maven.artifact.versioning.InvalidVersionSpecificationException;
import org.apache.maven.execution.MavenSession;
import org.apache.maven.model.Plugin;
import org.apache.maven.plugin.descriptor.PluginDescriptor;
import org.apache.maven.plugin.version.PluginVersionNotFoundException;
import org.apache.maven.plugin.version.PluginVersionResolutionException;

And then you can set headers in that *temporal* response object.

{* ../../docs_src/response_headers/tutorial002.py hl[1, 7:8] *}

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

{* ../../docs_src/first_steps/tutorial001.py hl[8] *}

Anda bisa menghasilkan `dict`, `list`, nilai singular seperti `str`, `int`, dll.

Anda juga bisa menghasilkan model Pydantic (anda akan belajar mengenai ini nanti).

Ada banyak objek dan model yang secara otomatis dikonversi ke JSON (termasuk ORM, dll). Anda bisa menggunakan yang anda suka, kemungkinan sudah didukung.

## Ringkasan

* Impor `FastAPI`.

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package model

import java.util.LinkedList

/**
 * Split a list of elements into nearly even sublist. If an element is too large, largeElementSplitFunction will be used to split the large element into several smaller pieces;

Although any other parameter declared normally (for example, the body with a Pydantic model) would still be validated, converted, annotated, etc.

But there are specific cases where it's useful to get the `Request` object.

## Use the `Request` object directly { #use-the-request-object-directly }

      <item index="4" class="java.lang.String" itemvalue="org.gradle.internal.scan.UsedByScanPlugin" />
      <item index="5" class="java.lang.String" itemvalue="org.gradle.api.model.ManagedObjectCreator" />
    </list>
    <pattern value="org.gradle.internal.service.ServiceRegistrationProvider" hierarchically="true" method="configure" />
  </component>

                            <goal>java</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <mainClass>model.FunctionalTestBucketGeneratorKt</mainClass>
                    <classpathScope>test</classpathScope>
                </configuration>
            </plugin>
        </plugins>
    </build>

    <dependencies>

```Python
from datetime import date

from pydantic import BaseModel

# Declareer een variabele als een str
# en krijg editorondersteuning in de functie
def main(user_id: str):
    return user_id


# Een Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Vervolgens kan je het op deze manier gebruiken:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

				} else {
					logger.Default.Warn(context.Background(), "In model %v, the hook function `%v(*gorm.DB) error` has an incorrect parameter type. The expected parameter type is `%v`, but the provided type is `%v`.", schema, cbName, expectedPkgPath, inVarPkg)
					// PASS
				}
			default: