// computed dynamically.
type DynamicType struct {
	miniExpr

	// RType is an expression that yields a *runtime._type value
	// representing the asserted type.
	//
	// BUG(mdempsky): If ITab is non-nil, RType may be nil.
	RType Node

	// ITab is an expression that yields a *runtime.itab value
	// representing the asserted type within the assertee expression's
	// original interface type.
	//

    %2 = "tf.IfRegion"(%arg0) ({
      %3 = "tf.StringToNumber"(%arg1) {out_type = f32} : (tensor<!tf_type.string>) -> tensor<f32>
      "tf.Yield"(%3) : (tensor<f32>) -> ()
     },  {
      %4 = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
      "tf.Yield"(%4) : (tensor<f32>) -> ()
    }) {is_stateless = true} : (tensor<i1>) -> (tensor<f32>)
    %5 = "tf.Identity"(%2) : (tensor<f32>) -> tensor<f32>

    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>):
    // CHECK: call @WhileOp_body
    // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>, tensor<i32>)
    %1:3 = func.call @WhileOp_body(%arg0, %arg1, %cst) : (tensor<*xi32>, tensor<*xf32>, tensor<i32>) -> (tensor<*xi32>, tensor<*xf32>, tensor<i32>)
    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()

  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
  }

    // CHECK: "tf.ReadVariableOp"([[name]])
    %cond = builtin.unrealized_conversion_cast to tensor<i1>
    %0 = "tf.IfRegion"(%cond) ({
      "tf.Yield"(%arg0) : (tensor<!tf_type.resource<tensor<10xf32>>>) -> ()
    }, {
      "tf.Yield"(%arg0) : (tensor<!tf_type.resource<tensor<10xf32>>>) -> ()
    }) { is_stateless = false} : (tensor<i1>) -> tensor<!tf_type.resource<tensor<10xf32>>>

  func.return
}

// -----

// Check that a tf_executor.yield parent is a tf_executor.island.
func.func @parent_is_island() {
  "tf.some_op"() ({
    tf_executor.yield
// expected-error@-1 {{'tf_executor.yield' op expects parent op 'tf_executor.island'}}
  }) : () -> ()
  func.return
}

// -----

// Check that an island yield matches the island results.

      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

        %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]]