O Python possui suporte para "dicas de tipo" ou "type hints" (também chamado de "anotações de tipo" ou "type annotations")

Esses **"type hints"** são uma sintaxe especial que permite declarar o <abbr title = "por exemplo: str, int, float, bool">tipo</abbr> de uma variável.

Ao declarar tipos para suas variáveis, editores e ferramentas podem oferecer um melhor suporte.

Python tiene soporte para "anotaciones de tipos" opcionales (también llamadas "type hints").

Estas **"anotaciones de tipos"** o type hints son una sintaxis especial que permite declarar el <abbr title="por ejemplo: str, int, float, bool">tipo</abbr> de una variable.

Al declarar tipos para tus variables, los editores y herramientas te pueden proporcionar un mejor soporte.

  @Generates
  Long generateLongObject() {
    return new Long(generateLong());
  }

  @Generates
  float generateFloat() {
    return generateInt();
  }

  @SuppressWarnings("removal") // b/321209431 -- maybe just use valueOf here?
  @Generates
  Float generateFloatObject() {
    return new Float(generateFloat());
  }

  @Generates
  double generateDouble() {
    return generateInt();
  }

      public Hasher putLong(long l) {
        for (Hasher hasher : hashers) {
          hasher.putLong(l);
        }
        return this;
      }

      @Override
      public Hasher putFloat(float f) {
        for (Hasher hasher : hashers) {
          hasher.putFloat(f);
        }
        return this;
      }

      @Override
      public Hasher putDouble(double d) {

pkg image/color, type NYCbCrA struct, A uint8
pkg image/color, type NYCbCrA struct, embedded YCbCr
pkg image/color, var NYCbCrAModel Model
pkg math/big, method (*Float) MarshalText() ([]uint8, error)
pkg math/big, method (*Float) UnmarshalText([]uint8) error
pkg math/big, method (*Int) Append([]uint8, int) []uint8
pkg math/big, method (*Int) Text(int) string
pkg math/rand, func Read([]uint8) (int, error)

from fastapi.responses import JSONResponse
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: Optional[float] = None


@app.api_route("/items/{item_id}", methods=["GET"])
def get_items(item_id: str):
    return {"item_id": item_id}


def get_not_decorated(item_id: str):
    return {"item_id": item_id}

pkg p1, const ConstChase2 = 11
pkg p1, const ConstChase2 ideal-int
pkg p1, const ConversionConst = 5
pkg p1, const ConversionConst MyInt
pkg p1, const FloatConst = 1.5  // 3/2
pkg p1, const FloatConst ideal-float
pkg p1, const StrConst = "foo"
pkg p1, const StrConst ideal-string
pkg p1, func Bar(int8, int16, int64)
pkg p1, func Bar1(int8, int16, int64) uint64
pkg p1, func Bar2(int8, int16, int64) (uint8, uint64)

```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):

{* ../../docs_src/path_params_numeric_validations/tutorial005_an_py39.py hl[10] *}

## Validations numériques : flottants, supérieur et inférieur

Les validations numériques fonctionnent également pour les valeurs `float`.

                            "name": "item_id",
                            "in": "path",
                        }
                    ],
                }
            },
            "/path/float/{item_id}": {
                "get": {
                    "responses": {
                        "200": {
                            "description": "Successful Response",