#
# 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);

     */
    public static void load(final Properties props, final Reader reader) {
        assertArgumentNotNull("props", props);
        assertArgumentNotNull("reader", reader);

        try {
            props.load(reader);
        } catch (final IOException e) {
            throw new IORuntimeException(e);
        }
    }

    /**

   * implement {@link #load load} instead.
   *
   * <p>The returned object is serializable if {@code supplier} is serializable.
   *
   * @param supplier the supplier to be used for loading values; must never return {@code null}
   * @return a cache loader that loads values by calling {@link Supplier#get}, irrespective of the
   *     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

   * implement {@link #load load} instead.
   *
   * <p>The returned object is serializable if {@code supplier} is serializable.
   *
   * @param supplier the supplier to be used for loading values; must never return {@code null}
   * @return a cache loader that loads values by calling {@link Supplier#get}, irrespective of the
   *     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 \\",

   * {@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

  __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 = (