if thresh := float64(d.trackBucket) / mudDegree; l < thresh {
		if r < thresh {
			d.trackSum += area
		} else {
			d.trackSum += area * (thresh - l) / (r - l)
		}
		if d.trackSum >= d.trackMass {
			// The tracked mass now falls in a different
			// bucket. Recompute the inverse cumulative sum.
			d.setTrackMass(d.trackMass)
		}
	}
}

// setTrackMass sets the mass to track the inverse cumulative sum for.
//

import org.gradle.initialization.StartParameterBuildOptions

class ConfigurationCacheInputListenerLifecycleIntegrationTest extends AbstractConfigurationCacheIntegrationTest {

    def "configuration inputs are tracked during task graph serialization"() {
        given:
        def configurationCache = newConfigurationCacheFixture()

        and: "a project that has a provider having undeclared configuration inputs, which is linked to a task"

 * limitations under the License.
 */

package org.gradle.internal.work;

import org.gradle.internal.operations.BuildOperationRef;

import java.util.List;

/**
 * Allows asynchronous work to be tracked based on the build operation it is associated with.
 */
public interface AsyncWorkTracker {
    enum ProjectLockRetention {
        RETAIN_PROJECT_LOCKS, RELEASE_PROJECT_LOCKS, RELEASE_AND_REACQUIRE_PROJECT_LOCKS
    }

  }

  auto tensor_type = mlir::dyn_cast_or_null<TensorType>(first_operand_type);
  if (!(tensor_type && tensor_type.hasRank())) {
    return op->emitError()
           << "cannot convert op with unranked or non-tensor input type "
           << tensor_type << ".";
  }

  int rank = tensor_type.getRank();
  if (rank <= partition_dim) {
    return op->emitError() << "cannot partition " << first_operand_type

	// this here and not e.g. on the trace reader.
	traceFrequency(gen)

	// Collect all the untraced Gs.
	type untracedG struct {
		gp           *g
		goid         uint64
		mid          int64
		stackID      uint64
		status       uint32
		waitreason   waitReason
		inMarkAssist bool
	}
	var untracedGs []untracedG
	forEachGRace(func(gp *g) {
		// Make absolutely sure all Gs are ready for the next

// RUN:   -split-input-file | FileCheck %s --check-prefix=CHECK-NO-UNPACK

// Tests that a simple dot_general (lifted as a function) with CustomAggregators
// around it is quantized. The resulting graph has quantized types unpacked into
// int ops.
func.func @main(%arg0: tensor<1x1024xf32>) -> tensor<1x3xf32> {
  %0 = "tf.Const"() <{value = dense<0.5> : tensor<1024x3xf32>}> : () -> tensor<1024x3xf32>

    return mlir::cast<IntegerAttr>(e).getValue() != 1;
  });
}

bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b) {
  // This would return false if we had unranked tensors (where they should
  // probably be considered as broadcastable), but given we are working with
  // attributes here that shouldn't be an issue,

        def dirContents = dir.list()
        if (dirContents) {
            printFileTree(dir)
        }
        // caches: Gradle user home caches, not present in project .gradle caches directory
        // native: unpacked native platform libraries, not present in project .gradle caches directory
        // .tmp: Temporary folder for worker classpath files and configuration caching report intermediate files, not present in project .gradle caches directory

		case http.StateNew:
			if _, exists := s.conns[c]; exists {
				panic("invalid state transition")
			}
			if s.conns == nil {
				s.conns = make(map[net.Conn]http.ConnState)
			}
			// Add c to the set of tracked conns and increment it to the
			// waitgroup.
			s.wg.Add(1)
			s.conns[c] = cs
			if s.closed {
				// Probably just a socket-late-binding dial from
				// the default transport that lost the race (and

  for (auto operand : op->getOperands()) {
    auto type = mlir::cast<mlir::TensorType>(operand.getType());
    if (type.hasRank()) {
      cost += GetRankedTensorSize(type);
    } else {
      // For unranked tensors, use the max size among the input tensors. This is
      // because the only dynamic information of the function should be the
      // input, so the size of dynamic tensors should be usually capped by