return func(yield func(K, V) bool) {
		for k, v := range m {
			if !yield(k, v) {
				return
			}
		}
	}
}

// Keys returns an iterator over keys in m.
// The iteration order is not specified and is not guaranteed
// to be the same from one call to the next.
func Keys[Map ~map[K]V, K comparable, V any](m Map) iter.Seq[K] {
	return func(yield func(K) bool) {
		for k := range m {

	return func(yield func(int, E) bool) {
		for i, v := range s {
			if !yield(i, v) {
				return
			}
		}
	}
}

// Backward returns an iterator over index-value pairs in the slice,
// traversing it backward. The indexes range from len(s)-1 down to 0.
func Backward[Slice ~[]E, E any](s Slice) iter.Seq2[int, E] {
	return func(yield func(int, E) bool) {
		for i := len(s) - 1; i >= 0; i-- {

    %add = "tf.AddV2"(%call, %call) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
    "tf.Yield"(%call, %add) : (tensor<4xf32>, tensor<4xf32>) -> ()
  },  {
    %call_0 = func.call @test_if_else1(%arg1) : (tensor<4xf32>) -> tensor<4xf32>
    "tf.Yield"(%call_0, %call_0) : (tensor<4xf32>, tensor<4xf32>) -> ()
  }) {is_stateless = false} : (tensor<i1>) -> (tensor<*xf32>, tensor<4xf32>)

	// in the middle of walking a node. Previously, incorrect handling of the
	// boolean returned by the yield function resulted in the iterator calling
	// yield for sibling nodes even after yield had returned false. With that
	// bug, this test failed with a runtime panic.
	src := "package p\ntype T struct {\n\tF int `json:\"f\"` // a field\n}\n"

	fset := token.NewFileSet()
	f, err := parser.ParseFile(fset, "", src, 0)
	if err != nil {

      // CHECK-SAME: device = "/device:test_device:0"
      %3 = "tf_device.launch_func"(%2) {device = "/device:test_device:0", func = @_func} : (tensor<?xf32>) -> tensor<?xf32>

      // CHECK: tf_executor.yield %[[CALL_OUTPUT]]
      tf_executor.yield %3 : tensor<?xf32>
    }
    tf_executor.fetch %1#0 : tensor<?xf32>
  }
  func.return %0 : tensor<?xf32>
}

func.func @_func(%arg0: tensor<?xf32>) -> tensor<?xf32> {

  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.GlobalIterId"() : () -> tensor<i64>
    "tf.Yield"(%arg2, %arg3) : (tensor<i32>, tensor<i32>) -> ()

        %5 = "tf.D"(%4) {device = "gpu0"} : (tensor<?xi32>) -> tensor<?xi32>
        // CHECK: tf_device.return %[[D_OUTPUT]]

        // CHECK: tf_executor.yield %[[GPU0_OUTPUT]]
        tf_executor.yield %5 : tensor<?xi32>
      }
      tf_executor.fetch %1#0 : tensor<?xi32>
    }
    func.return %0 : tensor<?xi32>
  }
}

// -----

        tf_device.return
      }
      tf_executor.yield
    }
    %1 = tf_executor.island {
      tf_device.replicate {n = 2 : i32, devices = {CORE_0 = ["/CPU:0", "/GPU:1"]}} {
        "tf_device.launch"() ({
          "tf.opA"() : () -> ()
          tf_device.return
        }) {device = "/TPU:1"} : () -> ()
        tf_device.return
      }
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return

    🎲 👆 💪 ▶️ 🆕 ⏬, ⚖️ 👆 🤚 🎡 🏃 ⚫️. 🤷

### 🔆 🔢

🥇 👜 👀, 👈 👥 ⚖ 🔁 🔢 ⏮️ `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)) }
            )
        }