},
		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.

			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

        @Override
        String toString() {
            return "reject " + version
        }
    }

    static class StrictPermutationsProvider implements Iterable<Candidate> {
        private final List<Batch> batches = []
        private int batchCount

        static StrictPermutationsProvider check(Map config) {
            new StrictPermutationsProvider().and(config)
        }

        failures.empty
    }

    def "executes tasks in a single batch in name order"() {
        Task a = task("a")
        Task b = task("b")
        Task c = task("c")

        when:
        populateAndExecute([b, c, a])

        then:
        executedTasks == [a, b, c]
        failures.empty
    }

    def "executes batches in order added"() {
        Task a = task("a")
        Task b = task("b")

// usage, and minimal GC pressure. It allocates events in batches so that even
// though we have to load the entire trace into memory, the conversion process
// shouldn't result in a doubling of memory usage, even if all converted events
// are kept alive, as we free batches once we're done with them.
//
// The conversion process is lossless.

package trace

import (
	"errors"

		o.BatchConfig.MaxBatchSize, "The maximum size of a batch. Only used in batch mode.")
	fs.DurationVar(&o.BatchConfig.MaxBatchWait, fmt.Sprintf("audit-%s-batch-max-wait", pluginName),
		o.BatchConfig.MaxBatchWait, "The amount of time to wait before force writing the "+
			"batch that hadn't reached the max size. Only used in batch mode.")

}

// FindInBatches finds all records in batches of batchSize
func (db *DB) FindInBatches(dest interface{}, batchSize int, fc func(tx *DB, batch int) error) *DB {
	var (
		tx = db.Order(clause.OrderByColumn{
			Column: clause.Column{Table: clause.CurrentTable, Name: clause.PrimaryKey},
		}).Session(&Session{})
		queryDB      = tx
		rowsAffected int64
		batch        int
	)

	// user specified offset or limit

	w := newTestWriter(t)
	return cfg.WithBuildXWriter(context.Background(), w)
}

// A testWriter is an io.Writer that writes to a test's log.
//
// The writer batches written data until the last byte of a write is a newline
// character, then flushes the batched data as a single call to Logf.
// Any remaining unflushed data is logged during Cleanup.
type testWriter struct {
	t testing.TB

	mu  sync.Mutex
	buf bytes.Buffer

// a placeholder for a differently encoded event that's actually present in the experimental data.
type ExperimentalData struct {
	// Batches contain the actual experimental data, along with metadata about each batch.
	Batches []ExperimentalBatch
}

// ExperimentalBatch represents a packet of unparsed data along with metadata about that packet.
type ExperimentalBatch struct {