putNext(this, next);
    }

    void unpark() {
      // This is racy with removeWaiter. The consequence of the race is that we may spuriously call
      // unpark even though the thread has already removed itself from the list. But even if we did
      // use a CAS, that race would still exist (it would just be ever so slightly smaller).
      Thread w = thread;
      if (w != null) {
        thread = null;

 * services.
 *
 * Please read <a href="../../../httpclient.html">Using jCIFS NTLM Authentication for HTTP Connections</a>.
 *
 * Warning: Do not use this if there is a chance that you might have multiple connections (even plain
 * HttpURLConnections, for the complete JRE) to the same host with different or mixed anonymous/authenticated
 * credentials. Authenticated connections can/will be reused.
 *

     */

    if (approxCmp > 0) {
      /*
       * The code is written so that even completely incorrect approximations will still yield the
       * correct answer eventually, but in practice this branch should almost never be entered, and
       * even then the loop should not run more than once.
       */
      do {
        approxLog10--;
        approxPow = approxPow.divide(BigInteger.TEN);

      putNext(this, next);
    }

    void unpark() {
      // This is racy with removeWaiter. The consequence of the race is that we may spuriously call
      // unpark even though the thread has already removed itself from the list. But even if we did
      // use a CAS, that race would still exist (it would just be ever so slightly smaller).
      Thread w = thread;
      if (w != null) {
        thread = null;

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

* **Deep Learning**: Esse é um subcampo do Machine Learning, então, o mesmo se aplica. A diferença é que não há apenas uma grande planilha com números para multiplicar, mas um grande conjunto delas, e em muitos casos, você utiliza um processador especial para construir e/ou usar esses modelos.

    and
    [CVE-2020-14155](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-14155).
*   Updates `sqlite3` to `3.44.0` to keep in sync with master branch.
*   Newer ROCm versions are supported on the 2.1 branch.

# Release 2.0.4

Note that this is the last patch release for the TensorFlow 2.0.x series.

## Bug Fixes and Other Changes

        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

2.  If you are changing the code and the docstring of a class/function/method,
    then you will need to
    [build TensorFlow from source](https://www.tensorflow.org/install/source).

    return x > 0 & (x & (x - 1)) == 0;
  }

  /**
   * Returns 1 if {@code x < y} as unsigned longs, and 0 otherwise. Assumes that x - y fits into a
   * signed long. The implementation is branch-free, and benchmarks suggest it is measurably faster
   * than the straightforward ternary expression.
   */
  @VisibleForTesting
  static int lessThanBranchFree(long x, long y) {
    // Returns the sign bit of x - y.

  }

  private static final class PaddedLock extends ReentrantLock {
    /*
     * Padding from 40 into 64 bytes, same size as cache line. Might be beneficial to add a fourth
     * long here, to minimize chance of interference between consecutive locks, but I couldn't
     * observe any benefit from that.
     */
    long unused1;
    long unused2;
    long unused3;

    PaddedLock() {
      super(false);

   * @IgnoreJRERequirement should be redundant with the one on Streams itself, but it's necessary as
   * of Animal Sniffer 1.24. Maybe Animal Sniffer processes this nested class before it processes
   * Streams and thus hasn't had a chance to see Streams's annotation?
   */
  @IgnoreJRERequirement
  private abstract static class MapWithIndexSpliterator<
          F extends Spliterator<?>,
          R extends @Nullable Object,