* 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,

		lo, cc := bits.Add(uint(z0), uint(c), 0)
		c, z[i] = Word(cc), Word(lo)
		c += z1
	}
	return
}

// q = ( x1 << _W + x0 - r)/y. m = floor(( _B^2 - 1 ) / d - _B). Requiring x1<y.
// An approximate reciprocal with a reference to "Improved Division by Invariant Integers
// (IEEE Transactions on Computers, 11 Jun. 2010)"
func divWW(x1, x0, y, m Word) (q, r Word) {
	s := nlz(y)
	if s != 0 {
		x1 = x1<<s | x0>>(_W-s)

   * 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

// 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.
 *