* penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *
   * This implementation uses a per-segment queue to record a memento of the additions, removals,
   * and accesses that were performed on the map. The queue is drained on writes and when it exceeds

                                        elements.getType().getElementType());
  return elements.reshape(new_type);
}

// This assumes that the bias is of shape NxCx1x1 and doesn't require transpose
// Its corresponding constraint is optimize_patterns.td:IsBiasShape()
ElementsAttr ReshapeNCHWBiasToNHWC(Value v, Attribute a) {
  auto elements = mlir::cast<DenseElementsAttr>(a);
  auto shape = mlir::cast<ShapedType>(v.getType()).getShape();

import groovy.lang.GroovySystem
import org.gradle.util.internal.VersionNumber

plugins {
    `java-platform`
}

group = "gradlebuild"

description = "Provides a platform that constrains versions of external dependencies used by Gradle"

// Here you should declare versions which should be shared by the different modules of buildSrc itself
val javaParserVersion = "3.18.0"
val groovyVersion = GroovySystem.getVersion()

					rs = tidy
				} else {
					conflict := Conflict{
						Path: mg.g.FindPath(func(m module.Version) bool {
							return m.Path == "go" && m.Version == v
						})[1:],
						Constraint: module.Version{Path: "go", Version: ld.TidyGoVersion},
					}
					msg := conflict.Summary()
					if cfg.BuildV {
						msg = conflict.String()
					}
					ld.error(errors.New(msg))
				}
			}

  // WhenUnsatisfiable indicates how to deal with a pod if it doesn't satisfy
  // the spread constraint.
  // - DoNotSchedule (default) tells the scheduler not to schedule it.
  // - ScheduleAnyway tells the scheduler to schedule the pod in any location,
  //   but giving higher precedence to topologies that would help reduce the
  //   skew.
  // A constraint is considered "Unsatisfiable" for an incoming pod

    as its predecessor. This is because the weight of cut edges is always
    minimized when the `Split` is with its predecessor. This colocation
    constraint will be used by the placer graph optimization to assign a device
    to the op.

    This pass should run in the export pipeline after tf-replicate-to-island so

	// WhenUnsatisfiable indicates how to deal with a pod if it doesn't satisfy
	// the spread constraint.
	// - DoNotSchedule (default) tells the scheduler not to schedule it.
	// - ScheduleAnyway tells the scheduler to schedule the pod in any location,
	//   but giving higher precedence to topologies that would help reduce the
	//   skew.
	// A constraint is considered "Unsatisfiable" for an incoming pod

		"issue20529.go",  // types2 does not have constraints on stack size
		"issue22200.go",  // types2 does not have constraints on stack size
		"issue22200b.go", // types2 does not have constraints on stack size
		"issue25507.go",  // types2 does not have constraints on stack size
		"issue20780.go",  // types2 does not have constraints on stack size
		"issue42058a.go", // types2 does not have constraints on channel element size

        endorseFrom2.targetComponent.nodes[0].ownStrictVersionConstraints != endorsedStrictVersions
    }

    def "uses empty strict version constraints object if there are no ancestor constraints"() {
        when:
        def edge = edge(root)
        def childNode = edge.targetComponent.nodes[0]
        collectOwnStrictVersions(root, [])

//
//	At 1000 items, it is roughly 5x slower (30ns vs 5ns).
type Set[T constraints.Ordered] struct {
	items []T
}

// NewPresorted creates a new Set with the given items.
// If items is not sorted or contains duplicates, this gives undefined behavior; use New instead.
func NewPresorted[T constraints.Ordered](items ...T) Set[T] {
	return Set[T]{items: items}
}