// can compute n / d as ⌊n * c / 2^F⌋, where c is ⌈2^F / d⌉ and F is
	// computed with:
	//
	// 	Algorithm 2: Algorithm to select the number of fractional bits
	// 	and the scaled approximate reciprocal in the case of unsigned
	// 	integers.
	//
	// 	if d is a power of two then
	// 		Let F ← log₂(d) and c = 1.
	// 	else
	// 		Let F ← N + L where L is the smallest integer

}

type Reporter struct {
	mu sync.RWMutex
	// map from connection id to latest nonce
	status map[string]string
	// map from nonce to connection ids for which it is current
	// using map[string]struct to approximate a hashset
	reverseStatus          map[string]sets.String
	inProgressResources    map[string]*inProgressEntry
	client                 v1.ConfigMapInterface
	cm                     *corev1.ConfigMap

  fun values(name: String): List<String> = commonValues(name)

  /**
   * Returns the number of bytes required to encode these headers using HTTP/1.1. This is also the
   * approximate size of HTTP/2 headers before they are compressed with HPACK. This value is
   * intended to be used as a metric: smaller headers are more efficient to encode and transmit.
   */
  fun byteCount(): Long {

   * that minimizes the need for remapping as {@code buckets} grows. That is, {@code
   * consistentHash(h, n)} equals:
   *
   * <ul>
   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *

  // Converts from read/write state that relates ops with the same parallel id
  // to a set of last accesses for use with other parallel ids. Reads/writes
  // between parallel ids are conservatively approximated as writes.
  absl::flat_hash_set<Operation*> GetLastWrites(ResourceId resource_id);

  // Sets the read/write state for ops within the same parallel id.
  void SetLastWrites(ResourceId resource_id,

   * that minimizes the need for remapping as {@code buckets} grows. That is, {@code
   * consistentHash(h, n)} equals:
   *
   * <ul>
   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *

						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

			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.