fmt.Fprintf(os.Stderr, "bad after panic")
		PASS = false
	}
}

// count returns the count and index for the counter at the specified line.
func count(line uint32) (uint32, int) {
	// Linear search is fine. Choose perfect fit over approximate.
	// We can have a closing brace for a range on the same line as a condition for an "else if"
	// and we don't want that brace to steal the count for the condition on the "if".

		"articles",
	}
	if len(children) != 32 {
		panic("bad len")
	}
	for _, n := range []int{2, 4, 8, 16, 32} {
		list := children[:n]
		b.Run(fmt.Sprintf("n=%d", n), func(b *testing.B) {

			b.Run("rep=linear", func(b *testing.B) {
				var entries []entry[string, any]
				for _, c := range list {
					entries = append(entries, entry[string, any]{c, nil})
				}
				b.ResetTimer()
				for i := 0; i < b.N; i++ {

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

	// single pass over the AST, but seldom is there more than one.)
	for v, stmt := range cancelvars {
		if ret := lostCancelPath(pass, g, v, stmt, sig); ret != nil {
			lineno := pass.Fset.Position(stmt.Pos()).Line
			pass.ReportRangef(stmt, "the %s function is not used on all paths (possible context leak)", v.Name())

			pos, end := ret.Pos(), ret.End()

		seenStartLine = seenStartLine || n.Info.StartLine != 0

		canonicalName := n.Info.Name
		// Create the key to the nodeMapKey.
		namedEdge := NamedCallEdge{
			CallerName:     canonicalName,
			CallSiteOffset: n.Info.Lineno - n.Info.StartLine,
		}

		for _, e := range n.Out {
			totalWeight += e.WeightValue()
			namedEdge.CalleeName = e.Dest.Info.Name
			// Create new entry or increment existing entry.

    return ensureInUnitRange(sumOfProductsOfDeltas / Math.sqrt(productOfSumsOfSquaresOfDeltas));
  }

  /**
   * Returns a linear transformation giving the best fit to the data according to <a
   * href="http://mathworld.wolfram.com/LeastSquaresFitting.html">Ordinary Least Squares linear
   * regression</a> of {@code y} as a function of {@code x}. The count must be greater than one, and

// a bit vector with (length of input) * (length of prog) bits,
// to make sure it never explores the same (character position, instruction)
// state multiple times. This limits the search to run in time linear in
// the length of the test.
//
// backtrack is a fast replacement for the NFA code on small
// regexps when onepass cannot be used.

package regexp

import (
	"regexp/syntax"
	"sync"
)

    }
    return this;
  }

  /**
   * Adds each member of {@code elements} as a candidate for the top {@code k} elements. This
   * operation takes amortized linear time in the length of {@code elements}.
   *
   * <p>If all input data to this {@code TopKSelector} is in a single {@code Iterable}, prefer

	if i.Address != 0 {
		name = append(name, fmt.Sprintf("%016x", i.Address))
	}
	if fun := i.Name; fun != "" {
		name = append(name, fun)
	}

	switch {
	case i.Lineno != 0:
		s := fmt.Sprintf("%s:%d", i.File, i.Lineno)
		if i.Columnno != 0 {
			s += fmt.Sprintf(":%d", i.Columnno)
		}
		// User requested line numbers, provide what we have.
		name = append(name, s)
	case i.File != "":

	// This is a bit complicated but the obvious alternative,
	// namely a hand-written search like in most shell glob matchers,
	// is too easy to make accidentally exponential.
	// Using package regexp guarantees linear-time matching.

	const vendorChar = "\x00"

	if vendorExclude && strings.Contains(pattern, vendorChar) {
		return func(name string) bool { return false }
	}

	re := regexp.QuoteMeta(pattern)
	wild := `.*`