OpBuilder builder(&island.GetBody(), island.GetBody().begin());
  tf_executor::YieldOp yield = island.GetYield();
  if (yield.getNumOperands() == 0) {
    builder.create<TF::NoOp>(island.getLoc(), TypeRange{}, ValueRange{});
  } else if (yield.getNumOperands() == 1) {
    Value operand = yield.getOperand(0);
    auto identity = builder.create<TF::IdentityOp>(island.getLoc(),

// tf_executor.island, tf.Identity, and tf_executor.yield).

// CHECK-LABEL: "func.func"
// CHECK: sym_name = "island_one_op_all_locs_same"
// CHECK:    "tf_executor.graph"() ({
// CHECK-NEXT:      "tf_executor.island"() ({
// CHECK-NEXT:        "tf.Identity"(%{{.*}}) : (tensor<f32>) -> tensor<f32> loc("identity@some_function")
// CHECK-NEXT:        "tf_executor.yield"(%{{.*}}) : (tensor<f32>) -> () loc("identity@some_function")

    // CHECK: tf_executor.island wraps "tf.IdentityN"
    %0:3 = tf_executor.island {
      %1:2 = "tf.IdentityN"(%arg0, %arg1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      tf_executor.yield %1#0, %1#1 : tensor<i32>, tensor<f32> loc("identity@some_function")
    } loc("identity@some_function")
    tf_executor.fetch
  }
  func.return
}

// CHECK-LABEL: func @island_no_wrap_print_mismatched_results

client = TestClient(app)


def test_dependency_contextvars():
    """
    Check that custom middlewares don't affect the contextvar context for dependencies.

    The code before yield and the code after yield should be run in the same contextvar
    context, so that request_state_context_var.reset(contextvar_token).

    If they are run in a different context, that raises an error.
    """

    scf.yield %applied_relu : !tfr.tensor
  } else {
    %is_relu6 = tfr.equal %act, %relu6 -> i1
    %res1 = scf.if %is_relu6 -> !tfr.tensor {
      %applied_relu6 = tfr.call @tf__relu6(%add) : (!tfr.tensor) -> !tfr.tensor
      scf.yield %applied_relu6 : !tfr.tensor
    } else {
      scf.yield %add : !tfr.tensor
    }
    scf.yield %res1 : !tfr.tensor
  }
  tfr.return %res : !tfr.tensor

// CHECK: tf_executor.island
  tf_executor.graph {
    %0 = tf_executor.island {
      tf_executor.yield
    }
    %1:2 = tf_executor.island {
      tf_executor.yield %arg0 : i32
    }
    %2 = tf_executor.island(%0) {
      %a = "tf.opA"(%1#0) : (i32) -> i32
      tf_executor.yield
    }
    tf_executor.fetch %2 : !tf_executor.control
  }
  func.return
}

// -----

// CHECK:     %{{.*}}:2 = "tfl.while"(%{{.*}}, %{{.*}}) ({
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):
// CHECK:       "tfl.yield"(%{{.*}}) : (tensor<i1>) -> ()
// CHECK:     },  {
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):
// CHECK:       "tfl.yield"(%{{.*}}, %{{.*}}) : (tensor<*xi32>, tensor<*xf32>) -> ()
// CHECK:     }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)

// CHECK-DAG:         "tfl.yield"(%[[VAL_1]]) : (tensor<i1>) -> ()
// CHECK-DAG:       },  {
// CHECK-DAG:       ^bb0(%[[A2_BODY:.*]]: tensor<i32>):
// CHECK-DAG:         %[[CST_4:.*]] = arith.constant dense<1> : tensor<i32>
// CHECK-DAG:         %[[VAL_2:.*]] = tfl.add %[[A2_BODY]], %[[CST_4]] {fused_activation_function = "NONE"} : tensor<i32>
// CHECK-DAG:         "tfl.yield"(%[[VAL_2]]) : (tensor<i32>) -> ()

    🎲 👆 💪 ▶️ 🆕 ⏬, ⚖️ 👆 🤚 🎡 🏃 ⚫️. 🤷

### 🔆 🔢

🥇 👜 👀, 👈 👥 ⚖ 🔁 🔢 ⏮️ `yield`. 👉 📶 🎏 🔗 ⏮️ `yield`.

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

🥇 🍕 🔢, ⏭ `yield`, 🔜 🛠️ **⏭** 🈸 ▶️.

& 🍕 ⏮️ `yield` 🔜 🛠️ **⏮️** 🈸 ✔️ 🏁.

### 🔁 🔑 👨‍💼

🚥 👆 ✅, 🔢 🎀 ⏮️ `@asynccontextmanager`.

👈 🗜 🔢 🔘 🕳 🤙 "**🔁 🔑 👨‍💼**".

            runPropertyAccessResolution(
                propertyAccess,
                onLocalValue = { yield(AssignmentResolution.ReassignLocalVal(it.localValue)) },
                onProperty = { yield(AssignmentResolution.AssignProperty(PropertyReferenceResolution(it.receiver, it.property))) },
                onExternalObject = { yield(AssignmentResolution.ReassignExternal(it)) }
            )
        }