@Override
              protected Schedule getNextSchedule() throws Exception {
                // Explicitly yield to increase the probability of a pathological scheduling.
                Thread.yield();
                return new Schedule(0, SECONDS);
              }
            };
          }
        };
    service.useBarriers = false;

              @Override
              protected Schedule getNextSchedule() throws Exception {
                // Explicitly yield to increase the probability of a pathological scheduling.
                Thread.yield();
                return new Schedule(0, SECONDS);
              }
            };
          }
        };
    service.useBarriers = false;

                val groupId = pluginTree.path.joinToString(".")
                val groupType = pluginGroupTypeName(pluginTree.path)
                val groupTypeSpec = typeSpecForPluginGroupType(groupType)
                yield(
                    PluginDependencySpecAccessor.ForGroup(
                        groupId,
                        ExtensionSpec(extensionName, extendedType, groupTypeSpec)
                    )
                )

private
val Project.hierarchy: Sequence<Project>
    get() = sequence {
        var project = this@hierarchy
        yield(project)
        while (project != project.rootProject) {
            project = project.parent!!
            yield(project)
        }
    }


private
val Project.isLocationAwareEditorHintsEnabled: Boolean

    %4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

      "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<128x1024xf32>):
      %1 = "tf.XlaSharding"(%arg3) <{_XlaSharding = "\0A\0B\0C", sharding = "\0A\0B\0C"}> {unspecified_dims = []} : (tensor<128x1024xf32>) -> tensor<128x1024xf32>
      %2 = "tf.Square"(%1) : (tensor<128x1024xf32>) -> tensor<128x1024xf32>
      "tf.Yield"(%arg2, %2) : (tensor<i32>, tensor<128x1024xf32>) -> ()

          tf_device.return %c, %b : tensor<f32>, tensor<i32>
        }) {device = "CPU:0"} : () -> (tensor<f32>, tensor<i32>)
        %d = "tf.opD"() : () -> tensor<i1>
        tf_executor.yield %a, %launch#0, %launch#1, %d :
                          tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
      }
    ```

    Will be transformed into:

    ```mlir
      %island:5 = tf_executor.island {

 * accounts. SIDs are commonly represented using a textual format such as
 * <tt>S-1-5-21-1496946806-2192648263-3843101252-1029</tt> but they may
 * also be resolved to yield the name of the associated Windows account
 * such as <tt>Administrators</tt> or <tt>MYDOM\alice</tt>.
 * <p>
 * Consider the following output of <tt>examples/SidLookup.java</tt>:
 * 
 * <pre>

    bool WrapsSingleOp();
  }];

  let hasCanonicalizer = 1;

  let hasFolder = 1;
}

def TfExecutor_YieldOp : TfExecutor_Op<"yield",
    [Terminator, ControlOperandsAfterAllData, HasParent<"IslandOp">]> {
  let summary = [{
    The `tf_executor.yield` operation terminates and returns values for the
    `tf_executor.island` operation.
  }];

  let arguments = (ins
    Variadic<AnyType>:$fetches

```

1. Das ist die Generatorfunktion. Es handelt sich um eine „Generatorfunktion“, da sie `yield`-Anweisungen enthält.
2. Durch die Verwendung eines `with`-Blocks stellen wir sicher, dass das dateiähnliche Objekt geschlossen wird, nachdem die Generatorfunktion fertig ist. Also, nachdem sie mit dem Senden der Response fertig ist.