sysctls := []v1.Sysctl{
		{
			Name:  "kernel.msgmax",
			Value: "8192",
		},
		{
			Name:  "kernel.shm_rmid_forced",
			Value: "1",
		},
		{
			Name:  "net.ipv4.conf.eno2/100.rp_filter",
			Value: "1",
		},
		{
			Name:  "net/ipv4/ip_local_port_range",
			Value: "1024 65535",
		},
	}
	exceptSysctls := []v1.Sysctl{
		{
			Name:  "kernel.msgmax",
			Value: "8192",
		},
		{

		{sysctl: "kernel.msgmax"},
		{sysctl: "kernel.sem"},
		{sysctl: "kernel/sem"},
	}
	invalid := []Test{
		{sysctl: "kernel.shm_rmid_forced", hostIPC: true},
		{sysctl: "net.ipv4.ip_local_port_range", hostNet: true},
		{sysctl: "foo"},
		{sysctl: "net.a.b.c", hostNet: false},
		{sysctl: "net.ipv4.ip_local_port_range.a.b.c", hostNet: false},
		{sysctl: "kernel.msgmax", hostIPC: true},

Matthew Dempsky <******@****.***> 1684455363 -0700

			maxEvent = mu1[len(mu1)-1].Time
		}
	}
	if maxMax := time.Duration(maxEvent - minEvent); xMax > maxMax {
		xMax = maxMax
	}
	// Compute MMU curve.
	logMin, logMax := math.Log(float64(xMin)), math.Log(float64(xMax))
	const samples = 100
	plot := make([][]float64, samples)
	for i := 0; i < samples; i++ {
		window := time.Duration(math.Exp(float64(i)/(samples-1)*(logMax-logMin) + logMin))
		if quantiles == nil {

		testNormalDistribution(t, numTestSamples, 0, 1, seed)
	}
}

func TestNonStandardNormalValues(t *testing.T) {
	sdmax := 1000.0
	mmax := 1000.0
	if testing.Short() {
		sdmax = 5
		mmax = 5
	}
	for sd := 0.5; sd < sdmax; sd *= 2 {
		for m := 0.5; m < mmax; m *= 2 {
			for _, seed := range testSeeds {
				testNormalDistribution(t, numTestSamples, m, sd, seed)
				if testing.Short() {
					break

func TestMaxObjectSize(t *testing.T) {
	sizes := []struct {
		isMax bool
		size  int64
	}{
		// Test - 1 - maximum object size.
		{
			true,
			globalMaxObjectSize + 1,
		},
		// Test - 2 - not maximum object size.
		{
			false,
			globalMaxObjectSize - 1,
		},
	}
	for i, s := range sizes {
		isMax := isMaxObjectSize(s.size)
		if isMax != s.isMax {
			t.Errorf("Test %d: Expected %t, got %t", i+1, s.isMax, isMax)

		testNormalDistribution(t, numTestSamples, 0, 1, seed)
	}
}

func TestNonStandardNormalValues(t *testing.T) {
	sdmax := 1000.0
	mmax := 1000.0
	if testing.Short() {
		sdmax = 5
		mmax = 5
	}
	for sd := 0.5; sd < sdmax; sd *= 2 {
		for m := 0.5; m < mmax; m *= 2 {
			for _, seed := range testSeeds {
				testNormalDistribution(t, numTestSamples, m, sd, seed)
				if testing.Short() {
					break

package rand

/*
 * Uniform distribution
 *
 * algorithm by
 * DP Mitchell and JA Reeds
 */

const (
	rngLen   = 607
	rngTap   = 273
	rngMax   = 1 << 63
	rngMask  = rngMax - 1
	int32max = (1 << 31) - 1
)

var (
	// rngCooked used for seeding. See gen_cooked.go for details.
	rngCooked [rngLen]int64 = [...]int64{

  MinMax calibration calculates the global min and global max values.

  global min = min of given sample inputs
  global max = max of given sample inputs
  """

  def get_min_max_value(self) -> tuple[float, float]:
    """Calculates the global min and max values.

    Returns:
      (min_value, max_value): Min and max calculated using MinMax
    """
    return (

message QuantizationInfo {
  // min/max of the per axis value range. To quantize the value, the metadata
  // of the target properties should be specified or read from the ops
  // quantization specification.
  message MinMax {
    float min = 1;
    float max = 2;
  }

  // Affine parameters to quantize the per axis value. The metadata of the
  // target properties should be specified as well.
  message AffineParams {