}

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

def TF_XlaSparseCoreFtrlOp : TF_Op<"XlaSparseCoreFtrl", []> {
  let summary = "aaa";

  let arguments = (ins
    TF_Float32Tensor:$embedding_table,
    TF_Float32Tensor:$accumulator,
    TF_Float32Tensor:$linear,
    TF_Float32Tensor:$learning_rate,
    TF_Int32Tensor:$indices,
    TF_Float32Tensor:$gradient,
    TF_Float32Tensor:$beta,
    TF_Float32Tensor:$learning_rate_power,

  }];

  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

	// Try to find the g that needs to be notified.
	// If it hasn't made it to the list yet we won't find it,
	// but it won't park itself once it sees the new notify number.
	//
	// This scan looks linear but essentially always stops quickly.
	// Because g's queue separately from taking numbers,
	// there may be minor reorderings in the list, but we
	// expect the g we're looking for to be near the front.

				j = h // preserves f(j) == true
			}
		}
		// i == j, f(i-1) == false, and f(j) (= f(i)) == true  =>  answer is i.

		// Having found the first, linear scan forward to find the last.
		// We could do a second binary search, but the caller is going
		// to do a linear scan anyway.
		for j := i; j < len(offs); j++ {
			typ := rtypeOff(section, offs[j])
			if stringFor(typ) != s {
				break
			}

union QuantizationDetails {
  CustomQuantization,
}

// Parameters for converting a quantized tensor back to float.
table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.

# does not run on Windows nodes.
#
# Note: This step is O(#nodes^2) as we check if each node is present in the list of succeeded nodes.
# Making it linear would add code complexity without much benefit (as it just takes ~1s for 5k nodes).
# Assumes:
#   NODE_NAMES
# Sets:
#   NON_LOGEXPORTED_NODES
function find_non_logexported_nodes() {

	if cheaprand()&uint32(oldC.Mask) != 0 {
		// As cache gets larger, choose to update it less often
		// so we can amortize the cost of building a new cache
		// (that cost is linear in oldc.Mask).
		return case_, tab
	}

	// Make a new cache.
	newC := buildInterfaceSwitchCache(oldC, t, case_, tab)

	// Update cache. Use compare-and-swap so if multiple threads

      }
      return false;
    }

    @Override
    public int hashCode() {
      return Arrays.hashCode(functions);
    }
  }

  /**
   * Linear CongruentialGenerator to use for consistent hashing. See
   * http://en.wikipedia.org/wiki/Linear_congruential_generator
   */
  private static final class LinearCongruentialGenerator {
    private long state;

  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.
   */