#
# 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()

        PropertiesUtil.load(properties, inputStreamReader);
    }

    /**
     * Test method for
     * {@link org.codelibs.core.io.PropertiesUtil#load(Properties, File)} .
     */
    @Test
    public void testLoadPropertiesFile() {
        final Properties properties = new Properties();
        PropertiesUtil.load(properties, inputFile);

   * {@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

    }

    /**
     * Wraps exception handling for {@link Properties#load(InputStream)}.
     * <p>
     * The input stream is not closed.
     * </p>
     *
     * @param props the property set (must not be {@literal null})
     * @param in the input stream (must not be {@literal null})
     */
    public static void load(final Properties props, final InputStream in) {
        assertArgumentNotNull("props", props);

 *
 * <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

 *
 * <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);
 *   }
 * };

		),
		"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 \\",

  __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