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.

3. **Actively collaborate with us.** We commit real time and...

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are

                }
            }
        }
        return hash;
    }

    /**
     * Encodes a similar document hash using GZIP compression and Base64 encoding.
     * This reduces storage space for hash values while maintaining uniqueness.
     *
     * @param hash the hash string to encode
     * @return the encoded hash string with prefix, or the original hash if encoding fails
     */

    @SuppressWarnings("SynchronizeOnNonFinalField")
    public T get() {
      // Another variant of Double Checked Locking.
      //
      // We use two volatile reads. We could reduce this to one by
      // putting our fields into a holder class, but (at least on x86)
      // the extra memory consumption and indirection are more
      // expensive than the extra volatile reads.

    @SuppressWarnings("SynchronizeOnNonFinalField")
    public T get() {
      // Another variant of Double Checked Locking.
      //
      // We use two volatile reads. We could reduce this to one by
      // putting our fields into a holder class, but (at least on x86)
      // the extra memory consumption and indirection are more
      // expensive than the extra volatile reads.

 * threads. By default, this ordering is not guaranteed. However, a queue constructed with fairness
 * set to {@code true} grants threads access in FIFO order. Fairness generally decreases throughput
 * but reduces variability and avoids starvation.
 *
 * <p>This class and its iterator implement all of the optional methods of the {@link
 * Collection} and {@link Iterator} interfaces.
 *
 * @author Doug Lea

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

# Debug config
common: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.

 * threads. By default, this ordering is not guaranteed. However, a queue constructed with fairness
 * set to {@code true} grants threads access in FIFO order. Fairness generally decreases throughput
 * but reduces variability and avoids starvation.
 *
 * <p>This class and its iterator implement all of the optional methods of the {@link
 * Collection} and {@link Iterator} interfaces.
 *
 * @author Doug Lea