reject.selector == complement

        where:
        selector         | complement
        '20'             | '!(20)'
        '[3,10]'         | '!([3,10])'
        '(,10)'          | '!((,10))'
        'latest.release' | '!(latest.release)'
        '1+'             | '!(1+)'
        '[3,)'           | '!([3,))'
    }

        System.setProperty("java.version", "9.0.4");
        assertEquals(9, JvmUtil.getJavaVersion());
        System.setProperty("java.version", "10.0.1");
        assertEquals(10, JvmUtil.getJavaVersion());
        System.setProperty("java.version", "11.0.1");
        assertEquals(11, JvmUtil.getJavaVersion());
    }

    public void test_filterJvmOptions() {
        final String[] args = new String[] { //

	{"//line foo.go:10:4\n", "foo.go:10:4:"},   // column means print a column
	{"//line foo.go:10:4\n\n", "foo.go:11:1:"}, // relative columns start at 1 after newline

	{"/*line :10*/", ":10:"},
	{"/*line :10:4*/", "filename:10:4"},
	{"/*line foo.go:10*/", "foo.go:10:"},
	{"/*line foo.go:10:4*/", "foo.go:10:4:"},
	{"/*line foo.go:10:4*/\n", "foo.go:11:1:"},
}

func main() {
	f, err := ioutil.TempFile("", "issue22662b.go")
	if err != nil {

  %y, %batch_mean, %batch_var, %reserve_1, %reserve_2, %reserve_3
    = "tf.FusedBatchNormV3"(%arg0, %arg1, %arg1, %arg1, %arg1)
       {
         data_format = "NHWC",
         epsilon = 1.001 : f32,
         exponential_avg_factor = 1.0 : f32,
         is_training = true
       }
        : (tensor<1x28x28x64xf32>, tensor<64xf32>, tensor<64xf32>,
           tensor<64xf32>, tensor<64xf32>)

	sum = x + y + carry
	// The sum will overflow if both top bits are set (x & y) or if one of them
	// is (x | y), and a carry from the lower place happened. If such a carry
	// happens, the top bit will be 1 + 0 + 1 = 0 (&^ sum).
	carryOut = ((x & y) | ((x | y) &^ sum)) >> 63
	return

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
}

// CHECK-LABEL: quantize_conv_per_tensor

		{"test when i.scale > scaled and result overflows", int64Amount{value: 876, scale: 30}, 4, 0, false},
		{"test when i.scale < 0 and fraction exists", int64Amount{value: 93, scale: -1}, 0, 10, true},
		{"test when i.scale < 0 and fraction doesn't exist", int64Amount{value: 100, scale: -1}, 0, 10, true},
		{"test when i.value < 0 and fraction exists", int64Amount{value: -1932, scale: 2}, 4, -20, true},

  public void testManyEqual() {
    assertThat(
            ComparisonChain.start()
                .compare(1, 1)
                .compare(1L, 1L)
                .compareFalseFirst(true, true)
                .compare(1.0, 1.0)
                .compare(1.0f, 1.0f)
                .compare("a", "a", Ordering.usingToString())
                .result())
        .isEqualTo(0);
  }

  public void testShortCircuitLess() {
    assertThat(

  %y, %batch_mean, %batch_var, %reserve_1, %reserve_2, %reserve_3
    = "tf.FusedBatchNormV3"(%arg0, %arg1, %arg1, %arg1, %arg1)
       {
         data_format = "NCHW",
         epsilon = 1.001 : f32,
         exponential_avg_factor = 1.0 : f32,
         is_training = true
       }
        : (tensor<1x64x28x28xf32>, tensor<64xf32>, tensor<64xf32>,
           tensor<64xf32>, tensor<64xf32>)

    ).isEqualTo("1080::8:800:200c:417a")

    assertThat("1080::8:800:200C:417A".toCanonicalHost()).isEqualTo("1080::8:800:200c:417a")
    assertThat("FF01::101".toCanonicalHost()).isEqualTo("ff01::101")
    assertThat(
      "0:0:0:0:0:FFFF:129.144.52.38".toCanonicalHost(),
    ).isEqualTo("129.144.52.38")

    assertThat("::FFFF:129.144.52.38".toCanonicalHost()).isEqualTo("129.144.52.38")