* Преобразование данных
* Валидацию данных
* Автоматическую документацию

## Особые типы и валидация { #special-types-and-validation }

Помимо обычных простых типов, таких как `str`, `int`, `float` и т.д., вы можете использовать более сложные простые типы, которые наследуются от `str`.

    }

    public void setBoost_Equal(Float boost) {
        setBoost_Term(boost, null);
    }

    public void setBoost_Equal(Float boost, ConditionOptionCall<TermQueryBuilder> opLambda) {
        setBoost_Term(boost, opLambda);
    }

    public void setBoost_Term(Float boost) {
        setBoost_Term(boost, null);
    }

    public void setBoost_Term(Float boost, ConditionOptionCall<TermQueryBuilder> opLambda) {

  @CanIgnoreReturnValue
  PrimitiveSink putInt(int i);

  /** Puts a long into this sink. */
  @CanIgnoreReturnValue
  PrimitiveSink putLong(long l);

  /** Puts a float into this sink. */
  @CanIgnoreReturnValue
  PrimitiveSink putFloat(float f);

  /** Puts a double into this sink. */
  @CanIgnoreReturnValue
  PrimitiveSink putDouble(double d);

  /** Puts a boolean into this sink. */
  @CanIgnoreReturnValue

                            <la:message key="labels.dict_mapping_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

// Create 1 dimensional tensor with values from `values`
TF_Tensor* Int32Tensor(const std::vector<int32_t>& values);

TF_Tensor* Int32Tensor(int32_t v);

TF_Tensor* DoubleTensor(double v);

TF_Tensor* FloatTensor(float v);

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,

from datetime import datetime

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


@app.webhooks.post("new-subscription")
def new_subscription(body: Subscription):
    """
    When a new user subscribes to your service we'll send you a POST request with this

  @CanIgnoreReturnValue
  public final Hasher putDouble(double d) {
    return putLong(Double.doubleToRawLongBits(d));
  }

  @Override
  @CanIgnoreReturnValue
  public final Hasher putFloat(float f) {
    return putInt(Float.floatToRawIntBits(f));
  }

  @Override
  @CanIgnoreReturnValue
  public Hasher putUnencodedChars(CharSequence charSequence) {
    for (int i = 0, len = charSequence.length(); i < len; i++) {

from fastapi import FastAPI
from fastapi.temp_pydantic_v1_params import Body
from pydantic.v1 import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    size: float


app = FastAPI()


@app.post("/items/")
async def create_item(item: Annotated[Item, Body(embed=True)]) -> Item:

                            <la:message key="labels.dict_stemmeroverride_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

    @Override
    protected String getQueryClassName() {
        return WildcardQuery.class.getSimpleName();
    }

    @Override
    public QueryBuilder execute(final QueryContext context, final Query query, final float boost) {
        if (query instanceof final WildcardQuery wildcardQuery) {
            if (logger.isDebugEnabled()) {
                logger.debug("WildcardQuery: query={}, boost={}", query, boost);
            }