Möglicherweise stellen Sie fest, dass Sie zum Erstellen einer Instanz einer Python-Klasse die gleiche Syntax verwenden.

Zum Beispiel:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

In diesem Fall ist `fluffy` eine Instanz der Klasse `Cat`.

Und um `fluffy` zu erzeugen, rufen Sie `Cat` auf.

And it will be annotated / documented accordingly too.

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

     * environments and only to carry test data.
     *
     * The server’s TLS certificate **does not need to be signed** by a trusted certificate
     * authority. Instead, it will trust any well-formed certificate, even if it is self-signed.
     * This is necessary for testing against localhost or in development environments where a
     * certificate authority is not possible.
     *

  // +optional
  repeated string resourceNames = 4;
}

// SelfSubjectAccessReview checks whether or the current user can perform an action.  Not filling in a
// spec.namespace means "in all namespaces".  Self is a special case, because users should always be able
// to check whether they can perform an action
message SelfSubjectAccessReview {
  // Standard list metadata.

    }

    // Close the writer to release its resources (such as deflaters).
    ignoreIoExceptions {
      writer.close()
    }

    // Close the socket to break out the reader thread, which will clean up after itself.
    ignoreIoExceptions {
      socket.close()
    }

    // Release the threads.
    writerQueue.shutdown()
    pushQueue.shutdown()
    settingsListenerQueue.shutdown()
  }

还有许多其他镜像用于不同的需要（例如数据库），例如：


* <a href="https://hub.docker.com/_/postgres" class="external-link" target="_blank">PostgreSQL</a>
* <a href="https://hub.docker.com/_/mysql" class="external-link" target="_blank">MySQL</a>
* <a href="https://hub.docker.com/_/mongo" class="external-link" target="_blank">MongoDB</a>
* <a href="https://hub.docker.com/_/redis" class="external-link" target="_blank">Redis</a>, etc.

Es ist das beliebteste Python-Framework und genießt großes Vertrauen. Es wird zum Aufbau von Systemen wie Instagram verwendet.

Ist relativ eng mit relationalen Datenbanken (wie MySQL oder PostgreSQL) gekoppelt, daher ist es nicht sehr einfach, eine NoSQL-Datenbank (wie Couchbase, MongoDB, Cassandra, usw.) als Hauptspeicherengine zu verwenden.

github.com/go-openapi/validate v0.24.0/go.mod h1:iyeX1sEufmv3nPbBdX3ieNviWnOZaJ1+zquzJEf2BAQ=
github.com/go-sql-driver/mysql v1.6.0/go.mod h1:DCzpHaOWr8IXmIStZouvnhqoel9Qv2LBy8hT2VhHyBg=
github.com/go-sql-driver/mysql v1.8.1 h1:LedoTUt/eveggdHS9qUFC1EFSa8bU2+1pZjSRpvNJ1Y=
github.com/go-sql-driver/mysql v1.8.1/go.mod h1:wEBSXgmK//2ZFJyE+qWnIsVGmvmEKlqwuVSjsCm7DZg=

        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,

### Migration

Check out the [Pydantic migration guide](https://docs.pydantic.dev/2.0/migration/).