return null;
            }
          });
    }
    assertEquals(allTasks.size() + 1, barrier.getParties());
    for (int i = 0; i < 1000; i++) {
      Collections.shuffle(allTasks);
      final AbstractFuture<String> future = new AbstractFuture<String>() {};
      currentFuture.set(future);
      for (Callable<?> task : allTasks) {

              return null;
            }
          });
    }
    assertEquals(allTasks.size() + 1, barrier.getParties());
    for (int i = 0; i < 1000; i++) {
      Collections.shuffle(allTasks);
      final AbstractFuture<String> future = new AbstractFuture<String>() {};
      currentFuture.set(future);
      for (Callable<?> task : allTasks) {

		askDisks = len(disks) // use all available drives
	}

	// However many we ask, versions must exist on ~50%
	listingQuorum := (askDisks + 1) / 2

	if askDisks > 0 && len(disks) > askDisks {
		rand.Shuffle(len(disks), func(i, j int) {
			disks[i], disks[j] = disks[j], disks[i]
		})
		fallbackDisks = disks[askDisks:]
		disks = disks[:askDisks]
	}

	// How to resolve results.

      return no_op();

    // For vectors, shuffle values by sorting instead of the obvious
    // Fisher-Yates algorithm. Fisher-Yates is simple to implement and correct,
    // but not easily parallelizable. For a sufficiently parallel architecture,
    // it is faster to sort many times, than Fisher-Yates shuffle once.
    if (input_rank == 1) {
      // Shuffle values by assigning each value a random key and sorting the

//	    the Go tree can run a sanity check but not spend time running
//	    exhaustive tests.
//
//	-shuffle off,on,N
//	    Randomize the execution order of tests and benchmarks.
//	    It is off by default. If -shuffle is set to on, then it will seed
//	    the randomizer using the system clock. If -shuffle is set to an
//	    integer N, then N will be used as the seed value. In both cases,

	if seed != -1 {
		rand.Seed(seed)
	}
	return rand.Intn(max-min) + min
}

// Randomizes the order of bytes in the byte array
// using Knuth Fisher-Yates shuffle algorithm.
func randomizeBytes(s []byte, seed int64) []byte {
	// special value -1 means no explicit seeding.
	if seed != -1 {
		rand.Seed(seed)
	}
	n := len(s)
	var j int

*   `tf.data`

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

		// Note: we load C symbols before Go symbols, so we can scan from the start.
		for i < len(textp) && (ldr.SubSym(textp[i]) != 0 || ldr.AttrSubSymbol(textp[i])) {
			i++
		}
		textp = textp[i:]
		r.Shuffle(len(textp), func(i, j int) {
			textp[i], textp[j] = textp[j], textp[i]
		})
	}

	text := ctxt.xdefine("runtime.text", sym.STEXT, 0)
	etext := ctxt.xdefine("runtime.etext", sym.STEXT, 0)

					"handSize": {
						SchemaProps: spec.SchemaProps{

      return ArraysAreCastCompatible(inferred, actual);
    }
  }];
}

def TF_ConjugateTransposeOp : TF_Op<"ConjugateTranspose", [Pure]> {
  let summary = [{
Shuffle dimensions of x according to a permutation and conjugate the result.
  }];

  let description = [{
The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: