//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test.py
//tensorflow/lite/python:interpreter
//tensorflow/lite/python:interpreter_test
//tensorflow/lite/python:interpreter.py

/// note

Als je een Python expert bent en alles al weet over type hints, sla dan dit hoofdstuk over.

///

## Motivatie

Laten we beginnen met een eenvoudig voorbeeld:

{* ../../docs_src/python_types/tutorial001.py *}


Het aanroepen van dit programma leidt tot het volgende resultaat:

```
John Doe
```

APPLE_BAZEL_FILES = [
    'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
    'tensorflow/lite/swift/BUILD',
    'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]

# List of files to move when building for iOS.
IOS_FILES = [
    'tensorflow/lite/objc/TensorFlowLiteObjC.podspec',
    'tensorflow/lite/swift/TensorFlowLiteSwift.podspec',
]


class UserInputError(Exception):

Probably in many cases the default will be that the proxy doesn't have a stripped path prefix.

	})
}

// Return the list of address that MinIO server needs to listen on:
//   - Returning 127.0.0.1 is necessary so Console will be able to send
//     requests to the local S3 API.
//   - The returned List needs to be deduplicated as well.
func getServerListenAddrs() []string {
	// Use a string set to avoid duplication
	addrs := set.NewStringSet()
	// Listen on local interface to receive requests from Console

		select {
		case listenCh <- *b:
		default:
			// Do not block on slow receivers.
			b.Recycle()
		}
		return nil
	})
}

// Listen - listen on peers.
func (client *peerRESTClient) Listen(ctx context.Context, listenCh chan<- []byte, v url.Values) {
	go func() {
		for {
			client.doListen(ctx, listenCh, v)
			select {
			case <-ctx.Done():
				return
			default:

    * Met validators kunnen complexe dataschema's duidelijk en eenvoudig worden gedefinieerd, gecontroleerd en gedocumenteerd als JSON Schema.
    * Je kunt diep **geneste JSON** objecten laten valideren en annoteren.
* **Uitbreidbaar**:
    * Met Pydantic kunnen op maat gemaakte datatypen worden gedefinieerd of je kunt validatie uitbreiden met methoden op een model dat is ingericht met de decorator validator.

	// global Trace system to send HTTP request/response
	// and Storage/OS calls info to registered listeners.
	globalTrace = pubsub.New[madmin.TraceInfo, madmin.TraceType](8)

	// global Listen system to send S3 API events to registered listeners
	globalHTTPListen = pubsub.New[event.Event, pubsub.Mask](0)

	// global console system to send console logs to
	// registered listeners

Here are some possible combinations and strategies:

* **Uvicorn** with `--workers`
    * One Uvicorn **process manager** would listen on the **IP** and **port**, and it would start **multiple Uvicorn worker processes**.
* **Kubernetes** and other distributed **container systems**
    * Something in the **Kubernetes** layer would listen on the **IP** and **port**. The replication would be by having **multiple containers**, each with **one Uvicorn process** running.

Aber standardmäßig **„funktioniert einfach alles“**.

### Validierung

* Validierung für die meisten (oder alle?) Python-**Datentypen**, hierzu gehören:
    * JSON Objekte (`dict`).
    * JSON Listen (`list`), die den Typ ihrer Elemente definieren.
    * Strings (`str`) mit definierter minimaler und maximaler Länge.
    * Zahlen (`int`, `float`) mit Mindest- und Maximal-Werten, usw.