for _, have := range counts {
							err := float64(have) - want
							χ2 += err * err
						}
						χ2 /= want
						samples[i] = χ2
					}

					// Check that our samples approximate the appropriate normal distribution.
					dof := float64(nfact - 1)
					expected := &statsResults{mean: dof, stddev: math.Sqrt(2 * dof)}
					errorScale := max(1.0, expected.stddev)

	// DEK entries, but that cache has an aggressive TTL to keep the size under control.
	// with DEK/seed reuse and no storage migration, the number of entries in this cache
	// would be approximated by unique key IDs used by the KMS plugin
	// combined with the number of server restarts.  If storage migration
	// is performed after key ID changes, and the number of restarts

// incrementally shortened q for a total of n + (n-1) + (n-2) ... + 2 + 1, or n(n+1)/2 divW()'s.
// Recursive conversion divides q by its approximate square root, yielding two parts, each half
// the size of q. Using the iterative method on both halves means 2 * (n/2)(n/2 + 1)/2 divW()'s

			s = fmt.Sprintf("%g", x)
		}
		return s
	}

	// Out of float64 range. Do approximate manual to decimal
	// conversion to avoid precise but possibly slow Float
	// formatting.
	// f = mant * 2**exp
	var mant big.Float
	exp := f.MantExp(&mant) // 0.5 <= |mant| < 1.0

	// approximate float64 mantissa m and decimal exponent d
	// f ~ m * 10**d

// integer arithmetic.  Let's instead compute 2^e/c
// for a value of e TBD (^ = exponentiation).  Then
//   ⎣x / c⎦ = ⎣x * (2^e/c) / 2^e⎦.
// Dividing by 2^e is easy.  2^e/c isn't an integer, unfortunately.
// So we must approximate it.  Let's call its approximation m.
// We'll then compute
//   ⎣x * m / 2^e⎦
// Which we want to be equal to ⎣x / c⎦ for 0 <= x < 2^n-1
// where n is the word size.
// Setting x = c gives us c * m >= 2^e.

import java.util.stream.Collector;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A Bloom filter for instances of {@code T}. A Bloom filter offers an approximate containment test
 * with one-sided error: if it claims that an element is contained in it, this might be in error,
 * but if it claims that an element is <i>not</i> contained in it, then this is definitely true.
 *

						for _, have := range counts {
							err := float64(have) - want
							χ2 += err * err
						}
						χ2 /= want
						samples[i] = χ2
					}

					// Check that our samples approximate the appropriate normal distribution.
					dof := float64(nfact - 1)
					expected := &statsResults{mean: dof, stddev: math.Sqrt(2 * dof)}
					errorScale := max(1.0, expected.stddev)

import java.math.RoundingMode;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A Bloom filter for instances of {@code T}. A Bloom filter offers an approximate containment test
 * with one-sided error: if it claims that an element is contained in it, this might be in error,
 * but if it claims that an element is <i>not</i> contained in it, then this is definitely true.
 *

	if err != nil {
		if !ignoreError {
			return err
		}
	} else {
		i.ResourceVersion = version
	}
	i.Object = obj
	return nil
}

// ObjectName returns an approximate form of the resource's kind/name.
func (i *Info) ObjectName() string {
	if i.Mapping != nil {
		return fmt.Sprintf("%s/%s", i.Mapping.Resource.Resource, i.Name)
	}
	gvk := i.Object.GetObjectKind().GroupVersionKind()

	This increases tracer overhead, but could be helpful as a workaround or for
	debugging unexpected regressions caused by frame pointer unwinding.

	traceadvanceperiod: the approximate period in nanoseconds between trace generations. Only
	applies if a program is built with GOEXPERIMENT=exectracer2. Used primarily for testing
	and debugging the execution tracer.