}
	m2 := (*T).PM
	for range m2 /* ERROR "cannot range over m2 (variable of type func(*T)): func must be func(yield func(...) bool): argument is not func" */ {
	}
	for range f1 /* ERROR "cannot range over f1 (value of type func()): func must be func(yield func(...) bool): wrong argument count" */ {
	}

        %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]]

      %0 = "arith.constant" () {value = dense<0> : tensor<i32>} : () -> tensor<i32> loc("Const")
      %1 = "tf.NotEqual"(%condArg0, %0) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
      "tf.Yield"(%1) : (tensor<i1>) -> ()
    },
    // body
    {
    ^bb0(%bodyArg0: tensor<*xi32>, %bodyArg1: tensor<*xf32>):
      %0 = "arith.constant" () {value = dense<1> : tensor<i32>} : () -> tensor<i32> loc("Const")

A primeira coisa a notar, é que estamos definindo uma função assíncrona com `yield`. Isso é muito semelhante à Dependências com `yield`.

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

A primeira parte da função, antes do `yield`, será  executada **antes** da aplicação inicializar.

E a parte posterior do `yield` irá executar **após** a aplicação ser encerrada.

### Gerenciador de Contexto Assíncrono

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

The first part of the function, before the `yield`, will be executed **before** the application starts.

And the part after the `yield` will be executed **after** the application has finished.

### Async Context Manager

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Use of public API is ok.

package main

import (
	"iter"
	"unique"
)

func seq(yield func(int) bool) {
	yield(123)
}

var s = "hello"

func main() {
	h := unique.Make(s)
	next, stop := iter.Pull(seq)
	defer stop()
	println(h.Value())
	println(next())
	println(next())

// Linkname coroswitch is not allowed, even if iter.Pull
// is instantiated in the same package.

package main

import (
	"iter"
	"unsafe"
)

func seq(yield func(int) bool) {
	yield(123)
}

func main() {
	next, stop := iter.Pull(seq)
	next()
	stop()
	coroswitch(nil)
}

//go:linkname coroswitch runtime.coroswitch

      %control = tf_executor.island {
        "tf.StatefulPartitionedCall"(%arg0) <{config = "", config_proto = "", executor_type = "", f = @partitioned}> : (tensor<*x!tf_type.resource>) -> ()
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

func (*T) PM() {}
func (T) M()   {}

func test() {
	for range T.M { // ERROR "cannot range over T.M \(value of type func\(T\)\): func must be func\(yield func\(...\) bool\): argument is not func"
	}
	for range (*T).PM { // ERROR "cannot range over \(\*T\).PM \(value of type func\(\*T\)\): func must be func\(yield func\(...\) bool\): argument is not func"
	}

      %3 = "tf_device.cluster"() ({
        %4 = "tf.B"(%2) : (tensor<?xi32>) -> tensor<?xi32>
        tf_device.return %4 : tensor<?xi32>
      }) {} : () -> tensor<?xi32>

      // CHECK: tf_executor.yield %[[CLUSTER_OUTPUT]]
      tf_executor.yield %3 : tensor<?xi32>
    }
    tf_executor.fetch %1#0 : tensor<?xi32>
  }
  func.return %0 : tensor<?xi32>
}

// CHECK: func private @[[CLUSTER]]