SmallVector<Attribute, 8> shuffled(values.size());

  for (size_t i = 0; i < permutation.size(); ++i) {
    for (size_t j = 0; j < inner_size; ++j) {
      shuffled[i * inner_size + j] = values[permutation[i] * inner_size + j];
    }
  }

  return ArrayAttr::get(attr.getContext(), shuffled);
}

// Shuffle ranked tensor dimensions according to the permutation.

		abc.Lookup("b"),
		xyz.Lookup("b"),
		uvw.Lookup("c"),
		gr.Lookup("φ"),
	}
	if len(data) != len(want) {
		t.Fatal("want and data must match")
	}
	if reflect.DeepEqual(data, want) {
		t.Fatal("data must be shuffled")
	}
	sort.Slice(data, func(i, j int) bool { return data[i].Less(data[j]) })
	if !reflect.DeepEqual(data, want) {
		t.Logf("want: %#v", want)
		t.Logf("data: %#v", data)
		t.Errorf("sorting failed")
	}

                    PluginTree.PluginSpec("flat-plugin.conflict", "IgnoredPlugin"),
                    PluginTree.PluginSpec("nested.plugin-a.conflict", "IgnoredPlugin")
                ).shuffled().asSequence()
            ),
            equalTo<Map<String, PluginTree>>(
                linkedMapOf(
                    flatPlugin.id to flatPlugin,
                    "nested" to PluginTree.PluginGroup(

	return shuffledPartsMetadata
}

// shuffleDisks - shuffle input disks slice depending on the
// erasure distribution. Return shuffled slice of disks with
// their expected distribution.
func shuffleDisks(disks []StorageAPI, distribution []int) (shuffledDisks []StorageAPI) {
	if distribution == nil {
		return disks
	}
	shuffledDisks = make([]StorageAPI, len(disks))
	// Shuffle disks for expected distribution.

// If we use all bits of hash value, the critical(last) card shuffled by
// Dealer will be uneven to 2:3 (first half:second half) at most,
// in order to reduce this unevenness to 32:33, we set MaxHashBits to 60 here.
const MaxHashBits = 60

// RequiredEntropyBits makes a quick and slightly conservative estimate of the number
// of bits of hash value that are consumed in shuffle sharding a deck of the given size

go test -c
exec ./m.test -test.shuffle=off
! stdout '^-test.shuffle '

exec ./m.test -test.shuffle=on
stdout '^-test.shuffle '

exec ./m.test -test.v -test.bench=. -test.shuffle=0 foo_test.go
stdout '^-test.shuffle 0'
stdout '(?s)TestTwo(.*)TestOne(.*)TestThree(.*)BenchmarkThree(.*)BenchmarkOne(.*)BenchmarkTwo'

exec ./m.test -test.v -test.bench=. -test.shuffle=123 foo_test.go
stdout '^-test.shuffle 123'

	}

	// already sorted
	data.initB()
	SortStableFunc(data, intPairCmp)
	if !IsSortedFunc(data, intPairCmp) {
		t.Errorf("Stable shuffled sorted %d ints (order)", n)
	}
	if !data.inOrder(false) {
		t.Errorf("Stable shuffled sorted %d ints (stability)", n)
	}

	// sorted reversed
	for i := 0; i < len(data); i++ {
		data[i].a = len(data) - i
	}
	data.initB()

  // Pad spatial dimensions.
  %4 = "tf.Pad"(%3, %0) : (tensor<?x3x224x224xf32>, tensor<4x2xi32>) -> tensor<?x3x230x230xf32>

  // Shuffled paddings.
  // CHECK: %[[PADDINGS:.*]] = "tf.Const"(){{.*}}[0, 0], [3, 3], [3, 3], [0, 0]
  // NOFOLD: %[[PADDING:.*]] = "tf.Const"(){{.*}}[0, 0], [0, 0], [3, 3], [3, 3]

		t.Errorf("Stable wasn't stable on %d ints", n)
	}

	// already sorted
	data.initB()
	Stable(data)
	if !IsSorted(data) {
		t.Errorf("Stable shuffled sorted %d ints (order)", n)
	}
	if !data.inOrder() {
		t.Errorf("Stable shuffled sorted %d ints (stability)", n)
	}

	// sorted reversed
	for i := 0; i < len(data); i++ {
		data[i].a = len(data) - i
	}
	data.initB()
	Stable(data)

	if err != nil {
		removeRoots(disks)
		t.Fatal(err)
	}
	defer removeRoots(disks)
	z := objLayer.(*erasureServerPools)
	testShuffleDisks(t, z)
}

// Test shuffleDisks which returns shuffled slice of disks for their actual distribution.
func testShuffleDisks(t *testing.T, z *erasureServerPools) {
	disks := z.serverPools[0].GetDisks(0)()
	distribution := []int{16, 14, 12, 10, 8, 6, 4, 2, 1, 3, 5, 7, 9, 11, 13, 15}