#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

	for i := range numReaders {
		wg.Add(1)
		go func(readerID int) {
			defer wg.Done()
			for range opsPerGoroutine {
				_ = httpStats.toServerHTTPStats(false)
				_ = httpStats.totalS3Requests.Load(false)
				_ = httpStats.currentS3Requests.Load(false)
				time.Sleep(1 * time.Microsecond)
			}
		}(i)
	}
	wg.Wait()

	finalStats := httpStats.toServerHTTPStats(false)
	totalRequests := 0

 *
 * <p>Most implementations will only need to implement {@link #load}. Other methods may be
 * overridden as desired.
 *
 * <p>Usage example:
 *
 * {@snippet :
 * CacheLoader<Key, Graph> loader = new CacheLoader<Key, Graph>() {
 *   public Graph load(Key key) throws AnyException {
 *     return createExpensiveGraph(key);
 *   }
 * };

   * {@link CacheLoader#load} calls to {@code loader}.
   */
  static <K, V> CacheLoader<K, V> bulkLoader(CacheLoader<K, V> loader) {
    checkNotNull(loader);
    return new CacheLoader<K, V>() {
      @Override
      public V load(K key) throws Exception {
        return loader.load(key);
      }

      @Override

		),
		"before.\n.1.\n.2.\n.3.\n.after.\n",
	},
	{
		"LOAD macro",
		lines(
			"#define LOAD(off, reg) \\",
			"\tMOVBLZX	(off*4)(R12),	reg \\",
			"\tADDB	reg,		DX",
			"",
			"LOAD(8, AX)",
		),
		"\n.\n.MOVBLZX.(.8.*.4.).(.R12.).,.AX.\n.ADDB.AX.,.DX.\n",
	},
	{
		"nested multiline macro",
		lines(
			"#define KEYROUND(xmm, load, off, r1, r2, index) \\",
			"\tMOVBLZX	(BP)(DX*4),	R8 \\",

	defer t.updating.Unlock()

	if time.Since(time.UnixMilli(t.lastUpdateMs.Load())) < ttl {
		// There is a new value, release lock and return it.
		if v = t.val.Load(); v != nil {
			return *v, nil
		}
	}

	if err := t.update(ctx); err != nil {
		var empty T
		return empty, err
	}

	return *t.val.Load(), nil
}

// Get will return a cached value or fetch a new one.

// Sets read deadline
func (c *DeadlineConn) setReadDeadline() {
	// Do not set a Read deadline, if upstream wants to cancel all reads.
	if c.readDeadline <= 0 || c.abortReads.Load() || c.infReads.Load() {
		return
	}

	c.mu.Lock()
	defer c.mu.Unlock()
	if c.abortReads.Load() {
		return
	}

	now := time.Now()
	if now.Sub(c.readSetAt) > updateInterval {
		c.Conn.SetReadDeadline(now.Add(c.readDeadline + updateInterval))

  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

# Load tensorflow-io-gcs-filesystem if enabled
if (_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "true" or
    _os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):
  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v2/3.
_tf_uses_legacy_keras = (

   * {@code key}, simply waits for that thread to finish and returns its loaded value. Note that
   * multiple threads can concurrently load values for distinct keys.
   *
   * <p>Caches loaded by a {@link CacheLoader} will call {@link CacheLoader#load} to load new values
   * into the cache. Newly loaded values are added to the cache using {@code

    AtomicInteger loadAllCount = new AtomicInteger();

    CacheLoader<Object, Object> baseLoader =
        new CacheLoader<Object, Object>() {
          @Override
          public Object load(Object key) {
            loadCount.incrementAndGet();
            return new Object();
          }

          @Override
          public ListenableFuture<Object> reload(Object key, Object oldValue) {