fail("Expected DecodingException");
        } catch (DecodingException expected) {
          // Don't assert on the expectedMessage; the messages for exceptions thrown from the
          // decoding stream may differ from the messages for the decode methods.
        } catch (IOException e) {
          fail("Expected DecodingException but got: " + e);
        }
      }
    };

```Python hl_lines="18  21  23"
{!../../docs_src/path_params/tutorial005.py!}
```

In Ihrem Client erhalten Sie eine JSON-Response, wie etwa:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Pfad Parameter die Pfade enthalten

Angenommen, Sie haben eine *Pfadoperation* mit einem Pfad `/files/{file_path}`.

* Eine beliebige Folge von Argumenten, die der Reihe nach an die Taskfunktion übergeben werden sollen (`email`).
* Alle Schlüsselwort-Argumente, die an die Taskfunktion übergeben werden sollen (`message="some notification"`).

## Dependency Injection

      // timed out. e.g. if we weren't event able to grab the lock within the timeout we would never
      // even check the guard. I don't think we care too much about this use case but it could lead
      // to a confusing error message.
      throw new TimeoutException("Timed out waiting for " + this + " to reach the RUNNING state.");
    }
  }

  @Override
  public final void awaitTerminated() {

  schemas:
    Error:
      type: object
      properties:
        error_code:
          type: string
          example: "bd6b0bd6-0144-40bc-9567-c5751dd8eecb"
        message:
          type: string
          example: "This is an error message."

                            </div>
                            <div class="card-body">
                                <div>
                                    <la:info id="msg" message="true">
                                        <div class="alert alert-info">${msg}</div>
                                    </la:info>
                                    <la:errors property="_global"/>

                            "items": {
                                "anyOf": [{"type": "string"}, {"type": "integer"}]
                            },
                        },
                        "msg": {"title": "Message", "type": "string"},
                        "type": {"title": "Error Type", "type": "string"},
                    },
                },
                "HTTPValidationError": {

                            "items": {
                                "anyOf": [{"type": "string"}, {"type": "integer"}]
                            },
                        },
                        "msg": {"title": "Message", "type": "string"},
                        "type": {"title": "Error Type", "type": "string"},
                    },
                },
                "HTTPValidationError": {

	}
	msg, err := target.toProducerMessage(eventData)
	if err != nil {
		return err
	}
	_, _, err = target.producer.SendMessage(msg)
	return err
}

// sendMultiple sends multiple messages to the kafka.
func (target *KafkaTarget) sendMultiple(events []event.Event) error {
	if target.producer == nil {
		return store.ErrNotConnected
	}
	var msgs []*sarama.ProducerMessage
	for _, event := range events {

internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access