在同样的位置，你用 `Ctrl+Space` 触发自动补全，就能看到：

<img src="/img/python-types/image02.png">

这样，你可以滚动查看选项，直到找到那个“看着眼熟”的：

<img src="/img/python-types/image03.png">

## 更多动机 { #more-motivation }

看这个已经带有类型提示的函数：

{* ../../docs_src/python_types/tutorial003_py310.py hl[1] *}

因为编辑器知道变量的类型，你不仅能得到补全，还能获得错误检查：

<img src="/img/python-types/image04.png">

现在你知道需要修复它，用 `str(age)` 把 `age` 转成字符串：

// A handle to a tensor on a device.
//
// Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape,
// type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors
// placed in the memory of different devices or remote address spaces.
typedef struct TFE_TensorHandle TFE_TensorHandle;

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(const TF_Tensor* t,

      val length = buf.readShort().toInt() and 0xffff

      if (type == TYPE_A || type == TYPE_AAAA) {
        val bytes = ByteArray(length)
        buf.read(bytes)
        result.add(InetAddress.getByAddress(bytes))
      } else {
        buf.skip(length.toLong())
      }
    }

    return result
  }

  @Throws(EOFException::class)
  private fun skipName(source: Buffer) {
    // 0 - 63 bytes

* Sie können alle gültigen Pydantic-Datentypen hier überprüfen: [Pydantic-Datentypen](https://docs.pydantic.dev/latest/usage/types/types/).

## Beispiel { #example }

Hier ist ein Beispiel für eine *Pfadoperation* mit Parametern, die einige der oben genannten Typen verwenden.

{* ../../docs_src/extra_data_types/tutorial001_an_py310.py hl[1,3,12:16] *}

# Tipos Avançados de Python { #advanced-python-types }

Aqui estão algumas ideias adicionais que podem ser úteis ao trabalhar com tipos em Python.

## Usando `Union` ou `Optional` { #using-union-or-optional }

Se, por algum motivo, seu código não puder usar `|`, por exemplo, se não for em uma anotação de tipo, mas em algo como `response_model=`, em vez de usar a barra vertical (`|`) você pode usar `Union` do `typing`.

# Gelişmiş Python Tipleri { #advanced-python-types }

Python tipleriyle çalışırken işinize yarayabilecek bazı ek fikirler.

## `Union` veya `Optional` Kullanımı { #using-union-or-optional }

Kodunuz herhangi bir nedenle `|` kullanamıyorsa — örneğin bir tip açıklamasında (type annotation) değil de `response_model=` gibi bir yerdeyse — dikey çizgi (`|`) yerine `typing` içindeki `Union`'ı kullanabilirsiniz.

    * Standart Python `Decimal`.
    * request'lerde ve response'larda `float` ile aynı şekilde işlenir.
* Geçerli tüm Pydantic veri tiplerini burada görebilirsiniz: [Pydantic veri tipleri](https://docs.pydantic.dev/latest/usage/types/types/).

## Örnek { #example }

Yukarıdaki tiplerden bazılarını kullanan parametrelere sahip bir örnek *path operation* şöyle:

{* ../../docs_src/extra_data_types/tutorial001_an_py310.py hl[1,3,12:16] *}

typedef struct TF_AttrMetadata {
  // A boolean: 1 if the attribute value is a list, 0 otherwise.
  unsigned char is_list;

  // Length of the list if is_list is true. Undefined otherwise.
  int64_t list_size;

  // Type of elements of the list if is_list != 0.
  // Type of the single value stored in the attribute if is_list == 0.
  TF_AttrType type;

  // Total size the attribute value.

import okhttp3.dnsoverhttps.DnsRecordCodec.TYPE_A
import okhttp3.dnsoverhttps.DnsRecordCodec.TYPE_AAAA
import okhttp3.dnsoverhttps.DnsRecordCodec.decodeAnswers
import okio.ByteString.Companion.decodeHex
import org.junit.jupiter.api.Test

class DnsRecordCodecTest {
  @Test
  fun testGoogleDotComEncoding() {
    val encoded = encodeQuery("google.com", TYPE_A)

    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

// A list of TF_ShapeAndType elements..