%1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %1 = "tf.OpB"(%arg2) {is_stateless = true} : (tensor<*xf32>) -> tensor<*xf32>
    %2 = "tf.OpC"(%arg3) {is_stateless = true} : (tensor<*xf32>) -> tensor<*xf32>
    "tf.Yield"(%1, %2) : (tensor<*xf32>, tensor<*xf32>) -> ()

// When called as seq(yield), seq calls yield(v) for each value v in the sequence,
// stopping early if yield returns false.
type Seq[V any] func(yield func(V) bool)

// Seq2 is an iterator over sequences of pairs of values, most commonly key-value pairs.
// When called as seq(yield), seq calls yield(k, v) for each pair (k, v) in the sequence,
// stopping early if yield returns false.

// CHECK: tf_executor.graph {
// CHECK:   %[[CONTROL:.*]] = tf_executor.island {
// CHECK:     tf_executor.yield
// CHECK:   }
// CHECK:   tf_executor.fetch %[[CONTROL]] : !tf_executor.control
// CHECK: }
// CHECK: return

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

from fastapi import Depends


async def dependency_a():
    dep_a = generate_dep_a()
    try:
        yield dep_a
    finally:
        dep_a.close()


async def dependency_b(dep_a: Annotated[DepA, Depends(dependency_a)]):
    dep_b = generate_dep_b()
    try:
        yield dep_b
    finally:
        dep_b.close(dep_a)


async def dependency_c(dep_b: Annotated[DepB, Depends(dependency_b)]):

      "tf.Yield"(%0) : (tensor<512x256xf32>) -> ()
    }, {
      "tf.Yield"(%arg2) : (tensor<512x256xf32>) -> ()
    }) { is_stateless = true}: (tensor<i1>) -> tensor<512x256xf32>
    "tf.Yield"(%1) : (tensor<512x256xf32>) -> ()
  }, {
    "tf.Yield"(%arg2) : (tensor<512x256xf32>) -> ()
  }) { is_stateless = true}: (tensor<i1>) -> tensor<512x256xf32>

      tf_executor.yield %launch#0, %launch#1: tensor<f32>, tensor<i32>
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK-NOT: tf_executor.island
// CHECK:      [[A:%.+]]:2, {{.*}} = tf_executor.island wraps "tf.opA"
// CHECK-SAME: device = "CPU:0"
// CHECK-NOT:  tf_device.launch
// CHECK-NOT:      tf_executor.yield [[A]]#1, [[A]]#0

    %output0, %control0 = tf_executor.island {
      %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %a : tensor<i32>
    }
    %output1, %control1 = tf_executor.island {
      %b = "tf.opB"(%output0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %b : tensor<i32>
    }
    tf_executor.fetch %output1 : tensor<i32>
  }
  func.return %graph_result : tensor<i32>
}

      tf_executor.yield %a, %launch#0, %launch#1, %c : tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK:      %[[A:.*]] = "tf.opA"
// CHECK:      %[[B:.*]]:2 = "tf.opB"(%[[A]])
// CHECK-SAME: device = "CPU:0"
// CHECK:      %[[C:.*]] = "tf.opC"
// CHECK-NOT:  "tf_device.launch"
// CHECK:      tf_executor.yield %[[A]], %[[B]]#1, %[[B]]#0, %[[C]]

      %3 = "tf.opA"(%arg0) : (tensor<i1>) -> tensor<i1>
      tf_executor.yield %3 : tensor<i1>
    }
    %2:2 = tf_executor.island(%1#1) {
      %4 = "tf.opB"() : () -> tensor<f32>
      tf_executor.yield %4 : tensor<f32>
    }
    tf_executor.fetch %2#0 : tensor<f32>
  }
  func.return %0 : tensor<f32>
}

// CHECK-LABEL: @func2

	})
	return err
}

func iterSimple(yield func(int) bool) {
	for range 3 {
		if !yield(5) {
			return
		}
	}
}

func iterNested(yield func(int) bool) {
	next, stop := iter.Pull(iterSimple)
	for {
		v, ok := next()
		if ok {
			if !yield(v) {
				stop()
			}
		} else {
			return
		}
	}
}

func iterCallback(yield func(int) bool) {
	for range 3 {