#endif  // !IS_MOBILE_PLATFORM

using tensorflow::string;

namespace {

string DeviceName(const tensorflow::Device* d) {
  return (d == nullptr) ? "cpu:0" : d->name();
}

// Annotate eager runtime construction context to the given `function_def` as
// an attribute.
void AnnotateEagerRuntimeConstructionContext(
    tensorflow::FunctionDef& function_def) {
  tensorflow::AttrValue value;
  SetAttrValue("kEagerRuntime", &value);

 * instance should be created every time the method is called.
 *
 * <p>For example, the subclass could pass a {@link java.util.TreeMap} during construction, and
 * {@link #createCollection()} could return a {@link java.util.TreeSet}, in which case the
 * multimap's iterators would propagate through the keys and values in sorted order.
 *

Docker et des outils similaires utilisent également un **cache interne** lors de la construction de l'image : si un fichier n'a pas changé depuis la dernière construction de l'image de conteneur, alors il va **réutiliser la même couche** créée la dernière fois, au lieu de recopier le fichier et créer une nouvelle couche à partir de zéro.

   * is {@code 60}, and the concurrency level is {@code 8}, then eight segments are created, each
   * having a hash table of size eight. Providing a large enough estimate at construction time
   * avoids the need for expensive resizing operations later, but setting this value unnecessarily
   * high wastes memory.
   *
   * @return this {@code CacheBuilder} instance (for chaining)

 * instance should be created every time the method is called.
 *
 * <p>For example, the subclass could pass a {@link java.util.TreeMap} during construction, and
 * {@link #createCollection()} could return a {@link java.util.TreeSet}, in which case the
 * multimap's iterators would propagate through the keys and values in sorted order.
 *

	// limit (8KB), we stop growing the source string once the limit
	// is reached and keep reusing the same source string - that
	// should therefore be always resident in the L1 cache - until we
	// have completed the construction of the result.
	// This yields significant speedups (up to +100%) in cases where
	// the result length is large (roughly, over L2 cache size).
	const chunkLimit = 8 * 1024
	chunkMax := n
	if chunkMax > chunkLimit {

   * appears that this pattern is not directly applicable. But we can use the ridiculous, devious
   * trick of throwing an exception in the middle of the construction of another exception. Hotspot
   * is fine with that.
   */

  /**
   * Ensures that {@code index} specifies a valid <i>element</i> in an array, list or string of size

   * appears that this pattern is not directly applicable. But we can use the ridiculous, devious
   * trick of throwing an exception in the middle of the construction of another exception. Hotspot
   * is fine with that.
   */

  /**
   * Ensures that {@code index} specifies a valid <i>element</i> in an array, list or string of size

### `tf.keras`:

*   Improvements from the Functional API refactoring:
    *   Functional model construction does not need to maintain a global
        workspace graph, removing memory leaks especially when building many
        models or very large models.
    *   Functional model construction should be ~8-10% faster on average.
    *   Functional models can now contain non-symbolic values in their call

     *     {@code key} parameter. That way, we prevent the key from being garbage collected in the
     *     case of weak keys. If we create a new entry with a key that is null at construction time,
     *     we're not sure if entry will necessarily ever be garbage collected.
     * @param newNext entry in the same bucket