^bb0(%barg1: tensor<!tf_type.variant<tensor<?x1xf32>>>): // no predeceessors
      %cond = "tf.false"():()-> tensor<i1>
      "tf.Yield"(%cond) : (tensor<i1>) -> ()
  }, {
    ^bb0(%barg1: tensor<!tf_type.variant<tensor<?x1xf32>>>): // no predeceessors
    "tf.Yield"(%barg1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>) -> ()

      // CHECK: tf.Yield
      "tf.Yield"(%recv) : (tensor<f32>) -> ()
    }, {
      // CHECK-NOT: _xla_token_input_nodes
      %add = "tf.Add"(%arg1, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>

      // CHECK: tf.Yield
      "tf.Yield"(%add) : (tensor<f32>) -> ()
    }) { is_stateless = true}: (tensor<i1>) -> tensor<f32>

  func.func @main() {
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

  func.func @main() {
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    tf_executor.graph {
      %control = tf_executor.island {
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

      tf_executor.yield %0 : tensor<f32>
    }
// Uses two islands for no other reason than preventing canonicalization from
// eliminating the graph entirely.
    %2:2 = tf_executor.island(%1#1) {
      %4 = "tf.opB"(%1#0) : (tensor<f32>) -> tensor<f32>
      tf_executor.yield %4 : tensor<f32>
    }
    tf_executor.fetch %2#0 : tensor<f32>
  }

### Lifespan-Funktion

Das Erste, was auffällt, ist, dass wir eine asynchrone Funktion mit `yield` definieren. Das ist sehr ähnlich zu Abhängigkeiten mit `yield`.

```Python hl_lines="14-19"
{!../../../docs_src/events/tutorial003.py!}
```

Der erste Teil der Funktion, vor dem `yield`, wird ausgeführt **bevor** die Anwendung startet.

Und der Teil nach `yield` wird ausgeführt, **nachdem** die Anwendung beendet ist.

### Asynchroner Kontextmanager

	}
	m2 := (*T).PM
	for range m2 /* ERROR "cannot range over m2 (variable of type func(*T)): func must be func(yield func(...) bool): argument is not func" */ {
	}
	for range f1 /* ERROR "cannot range over f1 (value of type func()): func must be func(yield func(...) bool): wrong argument count" */ {
	}

        %4 = "tf.B"(%2) : (tensor<?xi32>) -> tensor<?xi32>
        tf_device.return %4 : tensor<?xi32>
      }) {device = "/device:test_device:0"} : () -> tensor<?xi32>

      // CHECK: tf_executor.yield %[[LAUNCH_OUTPUT]]
      tf_executor.yield %3 : tensor<?xi32>
    }
    tf_executor.fetch %1#0 : tensor<?xi32>
  }
  func.return %0 : tensor<?xi32>
}

// CHECK: func private @[[LAUNCH]]

A primeira coisa a notar, é que estamos definindo uma função assíncrona com `yield`. Isso é muito semelhante à Dependências com `yield`.

```Python hl_lines="14-19"
{!../../../docs_src/events/tutorial003.py!}
```

A primeira parte da função, antes do `yield`, será  executada **antes** da aplicação inicializar.

E a parte posterior do `yield` irá executar **após** a aplicação ser encerrada.

### Gerenciador de Contexto Assíncrono

```Python hl_lines="14-19"
{!../../../docs_src/events/tutorial003.py!}
```

The first part of the function, before the `yield`, will be executed **before** the application starts.

And the part after the `yield` will be executed **after** the application has finished.

### Async Context Manager