}

// karatsubaLen computes an approximation to the maximum k <= n such that
// k = p<<i for a number p <= threshold and an i >= 0. Thus, the
// result is the largest number that can be divided repeatedly by 2 before
// becoming about the value of threshold.
func karatsubaLen(n, threshold int) int {
	i := uint(0)
	for n > threshold {
		n >>= 1
		i++
	}
	return n << i
}

func (z nat) mul(x, y nat) nat {

	// enough to justify the cost of the multiplications.

	// The threshold is selected experimentally as a linear function of n.
	threshold := n / 4

	// We calculate how many of the most-significant bits of the exponent we can
	// compute before crossing the threshold, and we do it with doublings.
	i := bits.UintSize
	for logR>>i <= threshold {
		i--
	}
	for k := uint(0); k < logR>>i; k++ {
		rr.Add(rr, m)
	}

    }

    public SuggestDeleteResponse deleteOldWords(final ZonedDateTime threshold) {
        final long start = System.currentTimeMillis();
        final String query = FieldNames.TIMESTAMP + ":[* TO " + threshold.toInstant().toEpochMilli() + "] NOT " + FieldNames.KINDS + ':'
                + SuggestItem.Kind.USER;
        deleteByQuery(query);

  // compilation is disabled. This will raise an error for kLazy even if we have
  // not yet hit the compilation threshold and no compilation happens this
  // round. This is to avoid non-determanism of when compilation is disallowed,
  // for example by changing the threshold.
  if (state == DeviceCompileState::kUncompiled && FailOnXlaCompilation()) {
    VLOG(1) << "XLA compilation disabled: " << function.name() << "\n"

        2. Get list of supported block configurations of the op.
        3. Calculate random sparsity of the weight.
          3.1. If sparsity level is below the encoding threshold, keep in dense.
          3.2. If sparsity level is above the encoding threshold, go to 4.
        4. Try to encode the weight with supported block configurations. If the
           weight was pruned with the same block config, the blocked sparsity level

// if 'x' and 'z' happen to share the same underlying storage.
// The overhead of the checking and branching is visible when 'z' are small (~5%),
// so set a threshold of 32, and remain the small-sized part entirely untouched.
TEXT ·addVW(SB),NOSPLIT,$0
	MOVD	z+0(FP), R3
	MOVD	z_len+8(FP), R0
	MOVD	x+24(FP), R1
	MOVD	y+48(FP), R2
	CMP	$32, R0

          << quantized_op->getName().getStringRef().str() << " because it has "
          << mlir::dyn_cast<DenseFPElementsAttr>(attr).size()
          << " elements which is fewer than the threshold("
          << quant_specs_.minimum_elements_for_weights << " elements).";
      return false;
    }

    if (is_per_channel_quantization) {
      quant_type = mlir::dyn_cast<quant::QuantizedType>(

    if (num_elements < num_elements_threshold) {
      op->emitRemark("Quantization is skipped because the op has ")
          << num_elements << " elements which is fewer than the threshold("
          << num_elements_threshold << " elements).";
      return failure();
    }

    return success();
  }

  // Apply quantization with the provided spec.

// RUN: tf-tfrt-opt --split-input-file -pass-pipeline='builtin.module(pre-parallel-tf-to-mlrt{use-tpu-host-allocator-for-inputs=true},tf-mlrt-parallelization{tfrt-cost-threshold=4},tf-to-mlrt)'  %s | FileCheck %s --dump-input=fail --dump-input-filter=all

func.func @callee(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>) {
  func.return %arg0: tensor<i32>
}

// CHECK-LABEL: func @batch_function
func.func @batch_function(%arg0: tensor<i32>) -> (tensor<i32>) {

		w.resultRun = 1
	}

	if (result == results.Failure && w.resultRun < int(w.spec.FailureThreshold)) ||
		(result == results.Success && w.resultRun < int(w.spec.SuccessThreshold)) {
		// Success or failure is below threshold - leave the probe state unchanged.
		return true
	}

	w.resultsManager.Set(w.containerID, result, w.pod)

	if (w.probeType == liveness || w.probeType == startup) && result == results.Failure {