x, err := strconv.ParseFloat(t.out, 64)
			if err != nil {
				println("bug120: strconv.Atof64", t.out)
				panic("fail")
			}
			println("\twant exact:", strconv.FormatFloat(x, 'g', 1000, 64))
			println("\tgot exact: ", strconv.FormatFloat(t.f, 'g', 1000, 64))
			ok = false
		}
	}
	if !ok {
		os.Exit(1)
	}

  // 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>

  func.func @matmul_with_reshape_disable_quantization(%arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<?x10xf32>) {
    %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false

  %0 = mhlo.constant dense<0.000000e+00> : tensor<32x10xf32>
  %1 = mhlo.constant dense<0.000000e+00> : tensor<32x28x28x5xf32>
  %2 = "tf.ReadVariableOp"(%arg4) : (tensor<!tf_type.resource<tensor<10xf32>>>) -> tensor<10xf32>
  %3 = "tf.ReadVariableOp"(%arg2) : (tensor<!tf_type.resource<tensor<5xf32>>>) -> tensor<5xf32>

  %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>>

    %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
    %cst_1 = arith.constant dense<1.0> : tensor<144x10xf32>
    %cst_2 = arith.constant dense<0.1> : tensor<10xf32>
    %0 = "quantfork.stats"(%arg0) {
      layerStats = dense<[-1.28, 1.28]> : tensor<2xf32>
    } : (tensor<*xf32>) -> tensor<*xf32>
    %1 = "tf.MaxPool"(%0) {

func.func @testCompatibleCastType(%arg0: tensor<?xf32>) -> (tensor<10xf32>, tensor<10xf32>) {
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<?xf32>) -> tensor<10xf32>
  %1 = "tf.Cast"(%arg0) {Truncate = true} : (tensor<?xf32>) -> tensor<10xf32>
  func.return %0, %1: tensor<10xf32>, tensor<10xf32>

  // CHECK: %0 = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<?xf32>) -> tensor<10xf32>

	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 {