* Sie nimmt jeden **Request** entgegen, der an Ihre Anwendung gesendet wird.
* Sie kann dann etwas mit diesem **Request** tun oder beliebigen Code ausführen.
* Dann gibt sie den **Request** zur Verarbeitung durch den Rest der Anwendung weiter (durch eine bestimmte *Pfadoperation*).
* Sie nimmt dann die **Response** entgegen, die von der Anwendung generiert wurde (durch eine bestimmte *Pfadoperation*).

            requirements**.txt
            pyproject.toml
      - name: Install docs extras
        run: uv pip install -r requirements-docs.txt
      # Install MkDocs Material Insiders here just to put it in the cache for the rest of the steps
      - name: Install Material for MkDocs Insiders
        if: ( github.event_name != 'pull_request' || github.secret_source == 'Actions' )
        run: uv pip install -r requirements-docs-insiders.txt
        env:

	xhttp "github.com/minio/minio/internal/http"
	ioutilx "github.com/minio/minio/internal/ioutil"
	"github.com/minio/minio/internal/logger"
	"github.com/minio/minio/internal/logger/message/audit"
	"github.com/minio/minio/internal/rest"
	"github.com/minio/mux"
	"github.com/minio/pkg/v3/certs"
	"github.com/minio/pkg/v3/env"
	xnet "github.com/minio/pkg/v3/net"
	"golang.org/x/oauth2"
)

const (
	slashSeparator = "/"
)

/// info

<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerasi (atau enum) tersedia di Python</a> sejak versi 3.4.

///

/// tip | Tips

"AlxexNet", "ResNet", dan "LeNet" adalah nama <abbr title="Secara teknis, arsitektur model Deep Learning">model</abbr> *Machine Learning*.

///

### Mendeklarasikan *parameter path*

import org.lastaflute.web.Execute;
import org.lastaflute.web.response.JsonResponse;

import jakarta.annotation.Resource;

/**
 * API action for admin related query management.
 * Provides REST endpoints for managing related search queries.
 */
public class ApiAdminRelatedqueryAction extends FessApiAdminAction {

    private static final Logger logger = LogManager.getLogger(ApiAdminRelatedqueryAction.class);

You can also use the *path operation decorator* parameters `response_model_include` and `response_model_exclude`.

They take a `set` of `str` with the name of the attributes to include (omitting the rest) or to exclude (including the rest).

This can be used as a quick shortcut if you have only one Pydantic model and want to remove some data from the output.

/// tip

---

"_We adopted the **FastAPI** library to spawn a **REST** server that can be queried to obtain **predictions**. [for Ludwig]_"

    public String createdTime;

    /** Search parameter: version number */
    public String versionNo;

    /**
     * Clears all pagination data and search parameters.
     * Resets counters, flags, and search form fields to their default values.
     */
    public void clear() {
        allRecordCount = 0;
        allPageCount = 0;
        existPrePage = false;
        existNextPage = false;

    public String[] groups;

    /** Version number for optimistic locking */
    public String versionNo;

    /**
     * Clears all pagination state and search criteria.
     * Resets the pager to its initial state with default values.
     */
    public void clear() {
        allRecordCount = 0;
        allPageCount = 0;
        existPrePage = false;
        existNextPage = false;

    for (int i = 0; i < bytesToCopy; i++) {
      buffer.put(readBuffer.get());
    }
    munch(); // buffer becomes empty here, since chunkSize divides bufferSize

    // Now process directly from the rest of the input buffer
    while (readBuffer.remaining() >= chunkSize) {
      process(readBuffer);
    }

    // Finally stick the remainder back in our usual buffer
    buffer.put(readBuffer);
    return this;