{\n  width: 100%;\n}\n\n.fade {\n  transition: opacity 0.15s linear;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .fade {\n    transition: none;\n  }\n}\n\n.fade:not(.show) {\n  opacity: 0;\n}\n\n.collapse:not(.show) {\n  display: none;\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  transition: height 0.35s ease;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .collapsing {\n    transition: none;\n  }\n}\n\n.collapsing.width {\n  width: 0;\n  height: auto;\n...

        and `tf.data.Dataset.with_options()` respectively.
    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.

     * meta-iterators rather than letting the nesting get arbitrarily deep.  This keeps each
     * operation O(1).
     */

    @CheckForNull private Iterator<? extends Iterator<? extends T>> topMetaIterator;

    // Only becomes nonnull if we encounter nested concatenations.
    @CheckForNull private Deque<Iterator<? extends Iterator<? extends T>>> metaIterators;

                    + Hexdump.toHexString((int) ( Math.random() * 0xFF ), 2);
        }

        /*
         * Create an NbtAddress for the local interface with
         * the name deduced above possibly with scope applied and
         * cache it forever.
         */
        Name localName = new Name(tc.getConfig(), localHostname, 0x00, tc.getConfig().getNetbiosScope());

# Old config for backward compatibility
build:nvcc_clang --config=cuda_nvcc

# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.

   *
   * <p><b>Java 8+ users:</b> Use a chain of calls to {@link Comparator#thenComparing(Comparator)},
   * or {@code comparatorCollection.stream().reduce(Comparator::thenComparing).get()} (if the
   * collection might be empty, also provide a default comparator as the {@code identity} parameter
   * to {@code reduce}).
   *
   * @param comparators the comparators to try in order
   */
  @GwtCompatible(serializable = true)

   * Returns a new immutable array containing the values in the specified range.
   *
   * <p><b>Performance note:</b> The returned array has the same full memory footprint as this one
   * does (no actual copying is performed). To reduce memory usage, use {@code subArray(start,
   * end).trimmed()}.
   */
  public ImmutableIntArray subArray(int startIndex, int endIndex) {
    Preconditions.checkPositionIndexes(startIndex, endIndex, length());

     independent of the terms and conditions of this Public License.


Section 8 -- Interpretation.

  a. For the avoidance of doubt, this Public License does not, and
     shall not be interpreted to, reduce, limit, restrict, or impose
     conditions on any use of the Licensed Material that could lawfully
     be made without permission under this Public License.

{\n  width: 100%;\n}\n\n.fade {\n  transition: opacity 0.15s linear;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .fade {\n    transition: none;\n  }\n}\n\n.fade:not(.show) {\n  opacity: 0;\n}\n\n.collapse:not(.show) {\n  display: none;\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  transition: height 0.35s ease;\n}\n\n@media (prefers-reduced-motion: reduce) {\n  .collapsing {\n    transition: none;\n  }\n}\n\n.dropup,\n.dropright,\n.dropdown,\n.dropleft {\n ...

* **Rápido de programar**: Incrementa la velocidad de desarrollo entre 200% y 300%. *
* **Menos errores**: Reduce los errores humanos (de programador) aproximadamente un 40%. *