_ = a == c
	}
}

const size = 16

type T1 struct {
	a [size]byte
}

func BenchmarkEqStruct(b *testing.B) {
	x, y := T1{}, T1{}
	x.a = [size]byte{1, 2, 3, 4, 5, 6, 7, 8}
	y.a = [size]byte{2, 3, 4, 5, 6, 7, 8, 9}

	for i := 0; i < b.N; i++ {
		f := x == y
		if f {
			println("hello")
		}
	}

    }
    def fileAccessTracker = Mock(FileAccessTracker)
    def cache = new DirectoryBuildCache(persistentCache, fileAccessTracker, ".failed")
    def key = TestHashCodes.hashCodeFrom(12345678)
    def hashCode = key.toString()

    def "does not store partial result"() {
        when:
        cache.store(key) { output ->
            // Check that partial result file is created inside the cache directory

			input:    []byte{1, 2, 3, 4},
			expected: []byte{2, 2, 3, 4},
		},
		{
			name:     "byteSliceArithmeticUint64",
			mutator:  byteSliceArithmeticUint64,
			input:    []byte{1, 2, 3, 4, 5, 6, 7, 8},
			expected: []byte{2, 2, 3, 4, 5, 6, 7, 8},
		},
		{
			name:     "byteSliceOverwriteInterestingUint8",
			mutator:  byteSliceOverwriteInterestingUint8,
			input:    []byte{1, 2, 3, 4},
			expected: []byte{255, 2, 3, 4},

  // CHECK-DAG: %[[RES:.*]] = mhlo.constant dense<{{\[\[\[\[}}1, 4, 9, 16], [5, 12, 21, 32]]]]> : tensor<1x1x2x4xi32>
  %cst0 = mhlo.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
  %cst1 = mhlo.constant dense<[[[[1, 2, 3, 4], [5, 6, 7, 8]]]]> : tensor<1x1x2x4xi32>
  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>
  %1 = mhlo.multiply %0, %cst1 : tensor<1x1x2x4xi32>

	out[1] = a + 0xFFFFFFFE00000000
	// ppc64x/power10:"ADD\t[$]1234567,"
	// ppc64x/power9:"ADDIS\t[$]19,", "ADD\t[$]-10617,"
	// ppc64x/power8:"ADDIS\t[$]19,", "ADD\t[$]-10617,"
	out[2] = a + 1234567
	// ppc64x/power10:"ADD\t[$]-1234567,"
	// ppc64x/power9:"ADDIS\t[$]-19,", "ADD\t[$]10617,"
	// ppc64x/power8:"ADDIS\t[$]-19,", "ADD\t[$]10617,"
	out[3] = a - 1234567
	// ppc64x/power10:"ADD\t[$]2147450879,"

  ExpectNodeEqual<double>(c.node(), {42.0, 42.0, 42.0, 42.0}, {2, 2});

  auto d = ops::Const(root, {"1", "2", "3", "4", "5", "6"}, {2, 3});
  TF_CHECK_OK(root.status());
  EXPECT_EQ(d.op().output_type(0), DT_STRING);
  ExpectNodeEqual<tstring>(d.node(), {"1", "2", "3", "4", "5", "6"}, {2, 3});
}

TEST(ConstOpTest, FromProto) {
  Scope root = Scope::NewRootScope();
  TensorProto proto;

	}{
		{
			description:   "One container",
			defaultCPUset: cpuset.New(0, 1, 2, 3, 4, 5, 6, 7, 8),
			assignments: map[string]map[string]cpuset.CPUSet{
				"pod": {
					"c1": cpuset.New(0, 1),
				},
			},
		},
		{
			description:   "Two containers",
			defaultCPUset: cpuset.New(0, 1, 2, 3, 4, 5, 6, 7, 8),
			assignments: map[string]map[string]cpuset.CPUSet{
				"pod": {

	PNOP                                    // 0700000000000000
	PSTB R1, $1, 12345678(R2)               // 061000bc9822614e
	PSTD R1, $1, 12345678(R2)               // 041000bcf422614e
	PSTFD F1, $1, 12345678(R2)              // 061000bcd822614e
	PSTFS F1, $1, 123456789(R7)             // 0610075bd027cd15
	PSTH R1, $1, 12345678(R2)               // 061000bcb022614e
	PSTQ R2, $1, 12345678(R2)               // 041000bcf042614e

  // CHECK: return %[[RES_TRANSPOSE]]

  %0 = "tf.Conv2D"(%input, %filter)
       {
         data_format = "NCHW",
         dilations = [1, 2, 3, 4],
         explicit_paddings = [1, 2, 3, 4, 5, 6, 7, 8],
         padding = "EXPLICIT",
         strides = [5, 6, 7, 8]
       } : (tensor<1x3x32x32xf32>, tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32>

  func.return %0 : tensor<1x8x7x6xf32>
}

		},
	}

	gmsaCredSpecName := "gmsa spec name"
	gmsaCredSpec := "credential spec"
	username := "ContainerAdministrator"
	asHostProcess := true
	pod := &v1.Pod{
		ObjectMeta: metav1.ObjectMeta{
			UID:       "12345678",
			Name:      "bar",
			Namespace: "new",
		},
		Spec: v1.PodSpec{
			Containers: []v1.Container{
				{
					Name:            "foo",
					Image:           "busybox",