%range_nume = "tf.Sub"(%r_max_f64, %r_min_f64) : (tensor<1xf64>, tensor<1xf64>) -> tensor<1xf64>
    %range_deno = "tf.Sub"(%i8_max_f64, %i8_min_f64) : (tensor<f64>, tensor<f64>) -> tensor<f64>

    %scale_f64 = "tf.Div"(%range_nume, %range_deno) : (tensor<1xf64>, tensor<f64>) -> tensor<1xf64>
    %scale = "tf.Cast"(%scale_f64) : (tensor<1xf64>) -> tensor<1xf32>

    // Add comparison with minimum if needed

  attributes {tf_saved_model.exported_names = ["assign_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.AssignVariableOp"([[name]]
    %0 = "tf.Const"() {type = tensor<10xf32>, value = dense<[0.,10.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32>} : () -> tensor<10xf32>
    "tf.AssignVariableOp"(%arg0, %0) : (tensor<!tf_type.resource<tensor<10xf32>>>, tensor<10xf32>) -> ()

  %0 = "tf.TensorListFromTensor"(%arg0, %arg1) : (tensor<3x10xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<10xf32>>>
  %1 = "tf.TensorListPushBack"(%0, %arg2) : (tensor<!tf_type.variant<tensor<10xf32>>>, tensor<10xf32>) -> tensor<!tf_type.variant<tensor<10xf32>>>
  %2 = "tf.TensorListStack"(%1, %arg1) : (tensor<!tf_type.variant<tensor<10xf32>>>, tensor<1xi32>) -> tensor<?x10xf32>

  // CHECK-NEXT: %[[ADDN:.*]] = "tf.AddN"(%[[UPDATE]], %[[BROADCAST]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
  %addn = "tf.AddN"(%set, %tl) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK-NEXT: %[[ZEROS_LIKE:.*]] = "tf.ZerosLike"(%[[ADDN]]) : (tensor<10xf32>) -> tensor<10xf32>

  // CHECK-NEXT: %[[UPDATE:.*]] = "tf.XlaDynamicUpdateSlice"(%[[READ_VAL]], %[[UPDATE_SLICE]], %[[READ_SIZE]]) : (tensor<10xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<10xf32>
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[BUFFER]], %[[UPDATE]]) : (tensor<!tf_type.resource<tensor<10xf32>>>, tensor<10xf32>) -> ()
  // CHECK-NEXT: %[[CONST1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>

  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>
  %1 = "tf.BiasAdd"(%0, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<10xf32>) -> tensor<*xf32>
  %2 = "tf.Relu6"(%1) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>

  %1 = "arith.constant"() {value = dense<0> : tensor<16xi64>} : () -> tensor<16xi64>
  %2 = "tfl.conv_2d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x8x!quant.uniform<i16:f32, 1.0>>, tensor<16x1x1x8x!quant.uniform<i8:f32, 1.0>>, tensor<16xi64>) -> tensor<1x32x32x16x!quant.uniform<i16:f32, 1.0>>

	initAtof()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		ParseFloat(benchmarksRandomBits[i%1024], 64)
	}
}

func BenchmarkAtof64RandomFloats(b *testing.B) {
	initAtof()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		ParseFloat(benchmarksRandomNormal[i%1024], 64)
	}
}

func BenchmarkAtof64RandomLongFloats(b *testing.B) {
	initAtof()

			[]net.Addr{
				mustParseIPAddr("8.8.8.8"),
				mustParseIPAddr("1000::"),
				mustParseIPAddr("192.168.1.1"),
			},
			sets.New("192.168.1.1"),
		},
		{
			"Cidr IPv6",
			newCidrValidator("1000::/64"),
			[]net.Addr{
				mustParseIPAddr("8.8.8.8"),
				mustParseIPAddr("1000::"),
				mustParseIPAddr("192.168.1.1"),
			},
			sets.New("1000::"),
		},
	}

	for _, tc := range testCases {

}

// TokenVolumeSource returns the projected volume source for service account token.
func TokenVolumeSource() *api.ProjectedVolumeSource {
	return &api.ProjectedVolumeSource{
		// explicitly set default value, see #104464
		DefaultMode: pointer.Int32(corev1.ProjectedVolumeSourceDefaultMode),
		Sources: []api.VolumeProjection{
			{
				ServiceAccountToken: &api.ServiceAccountTokenProjection{
					Path:              "token",