// This is O(N), but we expect haystack to be small;
// so small that we expect a linear search to be faster
func containsNumber(haystack []int, needle int) bool {
	for _, v := range haystack {
		if v == needle {
			return true
		}
	}
	return false
}

// This is O(N), but we expect serviceNodePorts to be small;
// so small that we expect a linear search to be faster

	//   consisting entirely of two hyphen characters ("-",
	//   decimal value 45) followed by the boundary parameter
	//   value from the Content-Type header field, optional linear
	//   whitespace, and a terminating CRLF.
	if !bytes.HasPrefix(line, r.dashBoundary) {
		return false
	}
	rest := line[len(r.dashBoundary):]
	rest = skipLWSPChar(rest)

		return value
	}

}

// allocateFromRange allocates an empty IP address from the range of
// IPs between the first and last address (both included), starting
// from the start address.
// TODO: this is a linear search, it can be optimized.
func (a *Allocator) allocateFromRange(iterator func() netip.Addr, svc *api.Service) (net.IP, error) {
	for {
		ip := iterator()
		if !ip.IsValid() {
			break
		}

	if err != nil {
		t.Fatalf("%s", err)
	}

	lineStr := ""
	if line != 0 {
		lineStr = fmt.Sprintf(" (from line %d)", line)
	}
	if diff := cmp.Diff(expected, result); diff != "" {
		t.Errorf("rules do not match%s:\ndiff:\n%s\nfull result:\n```\n%s```", lineStr, diff, result)
	}

	if checkConsistency {
		err = checkIPTablesRuleJumps(expected)

   * equivalently, if this inverse view contains a key that is equal to {@code value}).
   *
   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override

  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor. Always returns at least setSize + 2.
   */
  @VisibleForTesting

		arch:  arch,
		cconv: cconv,
	})
}

type regSet struct {
	lo    int
	hi    int
	Rconv func(int) string
}

// Few enough architectures that a linear scan is fastest.
// Not even worth sorting.
var regSpace []regSet

/*
	Each architecture defines a register space as a unique
	integer range.

  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a
  reasonable contribution (meaning it is not a single liner cosmetic PR).

**2. Valid?**

-   If the PR passes all the quality checks then we go ahead and assign a
    reviewer.
-   If the PR didn't meet the validation criteria, we request for additional

}

LogicalResult ClusteringState::VerifyDominanceProperty(
    unsigned src_root, unsigned dst_root, Operation *insertion_point) {
  // TODO(ezhulenev): Optimize this linear scan with a map lookup.
  for (auto &member : members) {
    unsigned root = FindRoot(member.root);
    if (root != src_root) continue;

    // Block arguments do not really participate in clustering, they are only

  }];

  let description = [{
That is for rows we have grad for, we update var, accum and linear as follows:
accum_new = accum + grad * grad
linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
accum = accum_new
  }];

  let arguments = (ins