from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float


class ResponseMessage(BaseModel):
    message: str


@app.post("/items/", response_model=ResponseMessage)
async def create_item(item: Item):
    return {"message": "item received"}


@app.get("/items/", response_model=list[Item])
async def get_items():
    return [

    protected String allRecordCountRelation;

    /** Total number of pages based on record count and page size. */
    protected int allPageCount;

    /** Flag indicating whether a next page exists. */
    protected boolean existNextPage;

    /** Flag indicating whether a previous page exists. */
    protected boolean existPrevPage;

    /** Starting record number for the current page. */

    }

    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

    visibility = ["//visibility:public"],
)

# This flag disables generating tensorflow.lite.python under LiteRT repo.
# Avoid using flag for creating tflite wheels as tensorflow/lite is not yet fully split from tf.
config_setting(
    name = "disable_tf_lite_py",
    define_values = {"disable_tf_lite_py": "true"},
    visibility = ["//visibility:public"],
)

# This flag enables experimental MLIR support.
config_setting(

     */
    private int allRecordCount;

    /**
     * Total number of pages.
     */
    private int allPageCount;

    /**
     * Flag indicating whether a previous page exists.
     */
    private boolean existPrePage;

    /**
     * Flag indicating whether a next page exists.
     */
    private boolean existNextPage;

    /**
     * List of page numbers for pagination navigation.
     */

                            <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

* Конвертацію даних
* Валідацію даних
* Автоматичну документацію

## Спеціальні типи та валідація

Окрім звичайних типів, таких як `str`, `int`, `float`, та ін. Ви можете використовувати складніші типи, які наслідують `str`.