1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
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      "Legal Entity" shall mean the union of the acting entity and all

// separate type primarily to distinguish it from mean utilization,
// which is also a float64.
type totalUtil float64

func totalUtilOf(meanUtil float64, dur int64) totalUtil {
	return totalUtil(meanUtil * float64(dur))
}

// mean returns the mean utilization over dur.
func (u totalUtil) mean(dur time.Duration) float64 {
	return float64(u) / float64(dur)
}

	N := n * hashSize
	var c float64
	// find c such that Prob(mean-c*stddev < x < mean+c*stddev)^N > .9999
	for c = 0.0; math.Pow(math.Erf(c/math.Sqrt(2)), float64(N)) < .9999; c += .1 {
	}
	c *= 11.0 // allowed slack: 40% to 60% - we don't need to be perfectly random
	mean := .5 * REP
	stddev := .5 * math.Sqrt(REP)
	low := int(mean - c*stddev)
	high := int(mean + c*stddev)
	for i := 0; i < n; i++ {

               "intervalFactor": 2,
               "legendFormat": "{{cluster}} - {{namespace}}\n"
            }
         ],
         "title": "GC Duration Mean",
         "type": "timeseries"
      },
      {
         "collapsed": true,
         "gridPos": {
            "h": 1,
            "w": 24,
            "x": 0,
            "y": 17
         },

        @Scenario(type = PER_COMMIT, operatingSystems = [LINUX], testProjects = ["largeJavaMultiProject"],
            comment = "We only test the multi-project here since for the monolithic project we would have no cache hits. This would mean we actually would test incremental compilation."
        )
    ])
    def "clean assemble for abi change with local cache"() {
        given:
        setupTestProject(runner)

//
// When is_training is set to true, the given variance and mean are not used.
// In above calculation, they are replaced by new values. These new mean and
// variance are calculated as following:
// new_mean = mean(x, axis=[0, 1, 2])
// new_variance = mean(squared_difference(x, new_mean), axis=[0, 1, 2])
//
// The DDR rule for the is_training equals true case is as following:

			Pos:     n.Pos(),
			Message: fmt.Sprintf("conversion from %s to %s yields a string of one rune, not a string of digits (did you mean fmt.Sprint(x)?)", source, target),
		}

		if convertibleToRune {
			diag.SuggestedFixes = []analysis.SuggestedFix{
				{
					Message: "Did you mean to convert a rune to a string?",
					TextEdits: []analysis.TextEdit{
						{
							Pos:     arg.Pos(),
							End:     arg.Pos(),

	//
	// The n above was for the public interface of "if n <= 0,
	// Readdir returns all the FileInfo from the directory in a
	// single slice".
	//
	// But below, we use only negative to mean looping until the
	// end and positive to mean bounded, with positive
	// terminating at 0.
	if n == 0 {
		n = -1
	}

	for n != 0 {
		// Refill the buffer if necessary
		if d.bufp >= d.nbuf {
			d.bufp = 0
			var errno error

// the specified mean.
func fastexprand(mean int) int32 {
	// Avoid overflow. Maximum possible step is
	// -ln(1/(1<<randomBitCount)) * mean, approximately 20 * mean.
	switch {
	case mean > 0x7000000:
		mean = 0x7000000
	case mean == 0:
		return 0
	}

	// Take a random sample of the exponential distribution exp(-mean*x).

  Tf2XlaTypeConverter();
};

/// Adds the TF to XLA via TF2XLA rewrite patterns to the pattern list.
/// `prefer_tf2xla` means an op will be included iff it is not in
/// `MlirLegalizedUnderPreferTf2XlaSet`. `!prefer_tf2xla` mean an op will be
/// included if there is no native MLIR legalization for the op.
void PopulateLegalizeTfWithTf2XlaPatterns(llvm::StringRef device_type,
                                          RewritePatternSet& patterns,