#
# 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()

   * {@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);
 *   }
 * };

# learning applications.

load("@rules_cc//cc:cc_import.bzl", "cc_import")

# buildifier: disable=out-of-order-load
# copybara:comment_begin(oss-only)
load("//tensorflow:strict.default.bzl", "py_strict_library")

# copybara:comment_end
# buildifier: enable=out-of-order-load
load("@bazel_skylib//lib:selects.bzl", "selects")
load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting")

| `minio_node_cpu_avg_load5`           | CPU load average 5min.                     |
| `minio_node_cpu_avg_load5_avg`       | CPU load average 5min (avg).               |
| `minio_node_cpu_avg_load5_max`       | CPU load average 5min (max).               |
| `minio_node_cpu_avg_load5_perc`      | CPU load average 5min (percentage).        |

   * {@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

    }

    @Override
    public synchronized void load(final InputStream inStream) throws IOException {
        final Properties prop = new Properties();
        lastModified = propertiesFile.lastModified();
        prop.load(inStream);
        properties = prop;
    }

    @Override
    public synchronized void load(final Reader reader) throws IOException {