Escribes Python estándar con tipos así:

```Python
from datetime import date

from pydantic import BaseModel

# Declaras la variable como un str
# y obtienes soporte del editor dentro de la función
def main(user_id: str):
    return user_id


# Un modelo de Pydantic
class User(BaseModel):
    id: int
    name: str

You write standard Python with types:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

          }
        }
      }
    },
    /*
     * This one comes last to work around b/367716565. (One *possible* alternative would be to not
     * declare any methods in this enum, converting assertFailsToDecode into a static method that is
     * implemented with a `switch`. I haven't tested that.)
     */
    @GwtIncompatible // decodingStream(Reader)
    DECODING_STREAM {

    </server>
    -->
  </servers>

  <!-- mirrors
   | This is a list of mirrors to be used in downloading artifacts from remote repositories.
   |
   | It works like this: a POM may declare a repository to use in resolving certain artifacts.
   | However, this repository may have problems with heavy traffic at times, so people have mirrored
   | it to several places.
   |

Bạn viết chuẩn Python với kiểu dữ liệu như sau:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

如果你需要2分钟来学习如何使用 Python 类型（即使你不使用 FastAPI ），看看这个简短的教程：[Python Types](python-types.md){.internal-link target=_blank}。

编写带有类型标注的标准 Python：

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Je schrijft gewoon standaard Python met types:

```Python
from datetime import date

from pydantic import BaseModel

# Declareer een variabele als een str
# en krijg editorondersteuning in de functie
def main(user_id: str):
    return user_id


# Een Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date

	// of an error on a specific line.
	//
	// For each name, we generate these lines, where xxx is the index in toSniff plus one.
	//
	//	#line xxx "not-declared"
	//	void __cgo_f_xxx_1(void) { __typeof__(name) *__cgo_undefined__1; }
	//	#line xxx "not-type"
	//	void __cgo_f_xxx_2(void) { name *__cgo_undefined__2; }
	//	#line xxx "not-int-const"

    device_threads_.emplace_back(new DeviceThread(
        devices[device_index].c_str(), is_async, in_flight_nodes_limit));
  }
}

// Necessary for a unique_ptr to a forward-declared type.
ParallelDevice::~ParallelDevice() = default;

std::unique_ptr<ParallelTensor> ParallelDevice::CopyToParallelDevice(
    TFE_Context* context, TFE_TensorHandle* tensor, TF_Status* status) const {

     *       module-path, but is also compatible with placement on the class-path. Compatibility can
     *       be achieved, for example, by repeating in the {@code META-INF/services/} directory the services
     *       that are declared in the {@code module-info.class} file. In that case, the path type can be chosen
     *       by the plugin.</li>
     *   <li>If a {@link #patchModule(String)} is also set and the main JAR file is placed on the module-path,