lastTs  Time
	idx     int       // next index into []batch
	dataOff int       // next index into batch.data
	ev      baseEvent // last read event
}

func (b *batchCursor) nextEvent(batches []batch, freq frequency) (ok bool, err error) {
	// Batches should generally always have at least one event,
	// but let's be defensive about that and accept empty batches.
	for b.idx < len(batches) && len(batches[b.idx].data) == b.dataOff {
		b.idx++

      batching_queue = "batching_queue",
      enable_large_batch_splitting = false,
      Tin = [i32],
      Tcaptured = [i32],
      Tout = [i32]} : 1

  // Since batch function kernel scheduling is async, the above 2 batches can arrive in any order.
  // CHECK: Running @matmul_cpu
  // CHECK-NEXT: Tensor<type: int32 shape: [4,2] values: [[value_output:.*]]>

  // CHECK: Tensor<type: int32 shape: [2,2] values: [4 4]

		r.cpuSamples = r.gen.cpuSamples

		// Reset frontier.
		for m, batches := range r.gen.batches {
			bc := &batchCursor{m: m}
			ok, err := bc.nextEvent(batches, r.gen.freq)
			if err != nil {
				return Event{}, err
			}
			if !ok {
				// Turns out there aren't actually any events in these batches.
				continue
			}
			r.frontier = heapInsert(r.frontier, bc)
		}

	trace   *Trace
	gen     uint64
	batches []*Batch
	strings map[string]uint64
	stacks  map[stack]uint64

	// Options applied when Trace.Generate is called.
	ignoreStringBatchSizeLimit bool
	ignoreStackBatchSizeLimit  bool
}

// Batch starts a new event batch in the trace data.
//
// This is convenience function for generating correct batches.
func (g *Generation) Batch(thread trace.ThreadID, time Time) *Batch {

		},
		batches: make(map[ThreadID][]batch),
	}
	// Process the spilled batch.
	if spill != nil {
		g.gen = spill.gen
		if err := processBatch(g, *spill.batch); err != nil {
			return nil, nil, err
		}
		spill = nil
	}
	// Read batches one at a time until we either hit EOF or
	// the next generation.
	var spillErr error
	for {
		b, gen, err := readBatch(r)
		if err == io.EOF {
			break
		}

				}
				pluginName := resourceHandle.DriverName
				batches[pluginName] = append(batches[pluginName], claim)
			}

			return nil
		})
		if err != nil {
			return fmt.Errorf("locked cache operation: %w", err)
		}
	}

	// Call NodePrepareResources for all claims in each batch.
	// If there is any error, processing gets aborted.

import org.gradle.api.internal.artifacts.ivyservice.resolveengine.graph.DependencyGraphVisitor;

import java.util.LinkedHashSet;
import java.util.Set;

/**
 * A visitor which batches up all conflicts and reports them all at once at the end of
 * the resolution.
 */
public class FailOnVersionConflictGraphVisitor implements DependencyGraphVisitor {

			p.batchOffsets[raw.batchPid] = append(p.batchOffsets[raw.batchPid], bo)
			curP = raw.batchPid
		}

		batches := p.batchOffsets[curP]
		if len(batches) == 0 {
			return fmt.Errorf("read event %d with current P of %d, but P has no batches yet",
				raw.typ, curP)
		}
		batches[len(batches)-1].numEvents++

		if raw.typ == EvCPUSample {
			e := Event{Type: raw.typ}

			argOffset := 1

// traceStringTable is map of string -> unique ID that also manages
// writing strings out into the trace.
type traceStringTable struct {
	// lock protects buf.
	lock mutex
	buf  *traceBuf // string batches to write out to the trace.

	// tab is a mapping of string -> unique ID.
	tab traceMap
}

// put adds a string to the table, emits it, and returns a unique ID for it.

		if x.outUsed == len(x.out) {
			x.b.Encrypt(x.out, x.next)
			x.outUsed = 0
		}

		if x.decrypt {
			// We can precompute a larger segment of the
			// keystream on decryption. This will allow
			// larger batches for xor, and we should be
			// able to match CTR/OFB performance.
			copy(x.next[x.outUsed:], src)
		}
		n := subtle.XORBytes(dst, src, x.out[x.outUsed:])
		if !x.decrypt {
			copy(x.next[x.outUsed:], dst)
		}