///

## Reduzir duplicação

Reduzir a duplicação de código é uma das ideias principais no **FastAPI**.

A duplicação de código aumenta as chances de bugs, problemas de segurança, problemas de desincronização de código (quando você atualiza em um lugar, mas não em outros), etc.

E esses modelos estão compartilhando muitos dos dados e duplicando nomes e tipos de atributos.

        .build()
    val request = Request(server.url("/"))
    val response1 = client.newCall(request).execute()
    assertThat(response1.body.string()).isEqualTo("a")

    // Give the thread pool a chance to evict.
    Thread.sleep(500)
    val response2 = client.newCall(request).execute()
    assertThat(response2.body.string()).isEqualTo("b")
    assertThat(server.takeRequest().exchangeIndex).isEqualTo(0)

    void tryClose() {
        synchronized (LOCK) {

            /* Yes, there is the potential to drop packets
             * because we might close the socket during a
             * request. However the chances are slim and the
             * retry code should ensure the overall request
             * is serviced. The alternative complicates things
             * more than I think is worth it.
             */

				}
				return false
			case results <- result:
				bgSeq.countScanned(madmin.HealItemObject)
				return true
			}
		}

		// Note: updates from healEntry to tracker must be sent on results channel.
		healEntry := func(bucket string, entry metaCacheEntry) {
			defer jt.Give()

			if entry.name == "" && len(entry.metadata) == 0 {
				// ignore entries that don't have metadata.
				return
			}

        }
    }

    /**
     * Stop proactive health checking
     */
    public void stopProactiveHealthChecking() {
        if (healthCheckTask != null) {
            healthCheckTask.cancel(false);
            healthCheckTask = null;
            log.info("Stopped proactive health checking");
        }
    }

    /**
     * Perform proactive health check on all connections
     */

*   [Learn ML with TensorFlow](https://www.tensorflow.org/resources/learn-ml)
*   [TensorFlow Twitter](https://twitter.com/tensorflow)
*   [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)
*   [TensorFlow model optimization roadmap](https://www.tensorflow.org/model_optimization/guide/roadmap)
*   [TensorFlow White Papers](https://www.tensorflow.org/about/bib)

     * both say in their comments that they're not really sure what the right approach is. We go
     * with Chrome's behavior (which also experimentally seems to match what IE does), but if you
     * actually want to have a good chance of things working, please avoid double-quotes, newlines,
     * percent signs, and the like in your field names.
     */
    internal fun StringBuilder.appendQuotedString(key: String) {
      append('"')

   * structure is not necessarily related to the processing format.
   *
   * @since 8.0
   */
  public static final byte ETB = 23;

  /**
   * Cancel: A control character used to indicate that the data with which it is sent is in error or
   * is to be disregarded.
   *
   * @since 8.0
   */
  public static final byte CAN = 24;

  /**

    
    public void stopWatching(String directoryPath) {
        ChangeNotificationHandle handle = activeWatchers.remove(directoryPath);
        if (handle != null) {
            handle.active = false;
            // Cancel any pending notifications
            cancelNotification(handle);
        }
    }
    
    private void startAsyncNotification(ChangeNotificationHandle handle) {
        CompletableFuture.runAsync(() -> {

		p.shardSize = p.shardFileSize - p.offset
	}
	if p.shardSize == 0 {
		return newBuf, nil
	}

	readTriggerCh := make(chan bool, len(p.readers))
	defer xioutil.SafeClose(readTriggerCh) // close the channel upon return

	for i := 0; i < p.dataBlocks; i++ {
		// Setup read triggers for p.dataBlocks number of reads so that it reads in parallel.
		readTriggerCh <- true
	}

	disksNotFound := int32(0)