1. This is the generator function. It's a "generator function" because it contains `yield` statements inside.
2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.
3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function (`iterfile`).

		cancel()
	}()

	// Do listing...
	go func(o listPathOptions) {
		err := z.listMerged(listCtx, o, inCh)
		if err != nil {
			meta.setErr(err.Error())
		}
		o.debugln("listAndSave: listing", o.ID, "finished with ", err)
	}(*o)

	// Keep track of when we return since we no longer have to send entries to output.
	var funcReturned bool
	var funcReturnedMu sync.Mutex
	defer func() {
		funcReturnedMu.Lock()

    taskFaker.runTasks()
    client.listener.assertClosing(1000, "Goodbye!")
    client.webSocket!!.finishReader()
    taskFaker.runTasks()
    assertThat(client.closed).isTrue()

    // Server and client both finished closing, connection is closed.
    server.listener.assertClosed(1000, "Hello!")
    client.listener.assertClosed(1000, "Goodbye!")
  }

  @Test
  fun clientCloseCancelsConnectionAfterTimeout() {

       *   or it had already failed. (It couldn't have completed *successfully* or even had
       *   setFuture called on it: Neither of those can happen until we've finished processing all
       *   the completed inputs. And we're still processing at least one input, the one that
       *   triggered handleException.)
       *

    --executor-cores 1 \
    examples/jars/spark-examples*.jar 10
```

The job should produce an output as shown below. Note the value of pi in the output.

```
17/03/22 23:21:10 INFO DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 1.302805 s
Pi is roughly 3.1445191445191445
```

Job status can also be viewed in a browser by navigating to the YARN ResourceManager Web UI and clicking on job history server information.

	for scanner.Scan() {
		t.Fatal("read should fail")
	}
	err := scanner.Err()
	if err != io.ErrUnexpectedEOF {
		t.Errorf("unexpected error: %v", err)
	}
}

// Test that Scan finishes if we have endless empty reads.
type endlessZeros struct{}

func (endlessZeros) Read(p []byte) (int, error) {
	return 0, nil
}

func TestBadReader(t *testing.T) {
	scanner := NewScanner(endlessZeros{})

	err          error     // Sticky error.
	empties      int       // Count of successive empty tokens.
	scanCalled   bool      // Scan has been called; buffer is in use.
	done         bool      // Scan has finished.
}

// SplitFunc is the signature of the split function used to tokenize the
// input. The arguments are an initial substring of the remaining unprocessed

Puedes obtener el `root_path` actual utilizado por tu aplicación para cada request, es parte del diccionario `scope` (que es parte de la especificación ASGI).

Aquí lo estamos incluyendo en el mensaje solo con fines de demostración.

{* ../../docs_src/behind_a_proxy/tutorial001.py hl[8] *}

Luego, si inicias Uvicorn con:

<div class="termy">

```console
$ fastapi run main.py --root-path /api/v1

 *
 * <p><b>Note:</b> In general, {@code CharSource} is intended to be used for "file-like" sources
 * that provide readers that are:
 *
 * <ul>
 *   <li><b>Finite:</b> Many operations, such as {@link #length()} and {@link #read()}, will either
 *       block indefinitely or fail if the source creates an infinite reader.

 *
 * <p><b>Note:</b> In general, {@code CharSource} is intended to be used for "file-like" sources
 * that provide readers that are:
 *
 * <ul>
 *   <li><b>Finite:</b> Many operations, such as {@link #length()} and {@link #read()}, will either
 *       block indefinitely or fail if the source creates an infinite reader.