*   `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).

*   `tf.math`

	    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,

      misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);
    sampleData = sampleDataList.toArray(new String[sampleDataList.size()]);
  }

  // Since we can't pass a concrete SomeEnum.class directly, we need to use a raw type.
  @SuppressWarnings("unchecked")

      misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);
    sampleData = sampleDataList.toArray(new String[sampleDataList.size()]);
  }

  // Since we can't pass a concrete SomeEnum.class directly, we need to use a raw type.
  @SuppressWarnings("unchecked")

	if queuing.QueueLengthLimit <= 0 {
		allErrs = append(allErrs, field.Invalid(fldPath.Child("queueLengthLimit"), queuing.QueueLengthLimit, "must be positive"))
	}

	// validate input arguments for shuffle-sharding
	if queuing.Queues <= 0 {
		allErrs = append(allErrs, field.Invalid(fldPath.Child("queues"), queuing.Queues, "must be positive"))
	} else if queuing.Queues > priorityLevelConfigurationQueuingMaxQueues {

	// attribute of form DW_FORM_data4.
	R_DWARFFILEREF

	// Platform dependent relocations. Architectures with fixed width instructions
	// have the inherent issue that a 32-bit (or 64-bit!) displacement cannot be
	// stuffed into a 32-bit instruction, so an address needs to be spread across
	// several instructions, and in turn this requires a sequence of relocations, each
	// updating a part of an instruction. This leads to relocation codes that are

		// Run for a number of times, shuffling the input to ensure that output is consistent.
		for i := 0; i < 10; i++ {
			t.Run(fmt.Sprintf("test-%d-%s-run-%d", testID, tt.name, i), func(t *testing.T) {
				if i > 0 {
					rng.Shuffle(len(tt.m), func(i, j int) {
						tt.m[i], tt.m[j] = tt.m[j], tt.m[i]
					})
				}
				gotSelected, gotOk := tt.m.resolve(&tt.r)
				if gotOk != tt.wantOk {

 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the
 * data using an unpredictable seed should normally be enough to thwart this attack.
 *
 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes

		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.

 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the
 * data using an unpredictable seed should normally be enough to thwart this attack.
 *
 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes