*/
    ModelBuildingResult build(ModelBuildingRequest request, ModelBuildingResult result) throws ModelBuildingException;

    /**
     * Performs only the part of {@link ModelBuilder#build(ModelBuildingRequest)} that loads the raw model
     */
    Result<? extends Model> buildRawModel(File pomFile, int validationLevel, boolean locationTracking);

    private int numOfFragments;
    /** The offset for fragment positioning. */
    private int fragmentOffset;

    /**
     * Default constructor that initializes highlighting settings from Fess configuration.
     * Loads default values for type, fragment size, number of fragments, and fragment offset.
     */
    public HighlightInfo() {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();

                            <la:message key="labels.elevate_word_configuration"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item active"><la:link href="/admin/elevateword">
                                <la:message key="labels.elevate_word_link_list"/>

    // The values here look like 111...11101...010 in binary, where the initial 111...1110 takes
    // up exactly as many bits as can be represented in the significand (24 for float, 53 for
    // double). That final 0 should be rounded up to 1 because the remaining bits make that number
    // slightly nearer.
    long floatConversionTest = 0xfffffe8000000002L;

Diese **„Typhinweise“** oder -Annotationen sind eine spezielle Syntax, die es erlaubt, den <abbr title="zum Beispiel: str, int, float, bool">Typ</abbr> einer Variablen zu deklarieren.

Durch das Deklarieren von Typen für Ihre Variablen können Editoren und Tools bessere Unterstützung bieten.

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

    }

    /**
     * Initializes the analyzer settings.
     * If the analyzer settings index does not exist, it creates it with default settings and mappings.
     * It also loads analyzer names and field analyzer mappings.
     */
    public synchronized void init() {
        try {
            final IndicesExistsResponse response =

											${f:h(fe:label(countEntry.key))} 
											<span class="badge rounded-pill text-bg-secondary float-end">${f:h(countEntry.value)}</span>
									</la:link></li>
							</c:if>
						</c:forEach>
					</ul>
				</c:if>
			</c:forEach>
			<c:forEach var="facetQueryView" items="${fe:facetQueryViewList()}">

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()

bearer_scheme = HTTPBearer()


class Subscription(BaseModel):
    username: str
    monthly_fee: float
    start_date: datetime


@app.webhooks.post("new-subscription")
def new_subscription(
    body: Subscription, token: Annotated[str, Security(bearer_scheme)]
):
    """

         * Sets the weight for the request data.
         *
         * @param weight the weight to set
         * @return the current RequestDataContext instance
         */
        public RequestDataContext weight(final float weight) {
            data.setWeight(weight);
            return this;
        }

        /**
         * Builds and returns the constructed RequestData object.
         *