* This makes it possible to uniquely identify the snapshot.
     * <dl>
     *     <dt>Directories</dt>
     *     <dd>The combined hash of the children, calculated by appending the name and the hash of each child to a hasher.</dd>
     *     <dt>Regular Files</dt>
     *     <dd>The hash of the content of the file.</dd>
     *     <dt>Missing files</dt>
     *     <dd>A special signature denoting a missing file.</dd>
     * </dl>
     */

					group.label = l
					group.tot += dt
					group.size++
				} else {
					// start a new group
					if group.size > 1 {
						lines.add(prefix+group.label+"subtotal", 1, group.tot, tot, nil)
					}
					group.label = label
					group.tot = dt
					group.size = 1
				}

				// write phase
				lines.add(prefix+label, 1, dt, tot, events)
			}

			pt = qt
		}

@needs_pydanticv1
def test_pydanticv1():
    class ModelWithDatetimeField(BaseModel):
        dt_field: datetime

        class Config:
            json_encoders = {
                datetime: lambda dt: dt.replace(
                    microsecond=0, tzinfo=timezone.utc
                ).isoformat()
            }

    app = FastAPI()
    model = ModelWithDatetimeField(dt_field=datetime(2019, 1, 1, 8))

	steppingPeriod := time.Millisecond
	tro.Set(1)
	// `dt` is the admitted cumulative difference in fake time
	// since the start of the test.  "admitted" means this is
	// never allowed to decrease, which matches the designed
	// toleration for negative monotonic clock changes.
	var dt time.Duration
	// `t1` is the current fake time
	t1 := t0.Add(dt)

	cpuHog(20 * time.Millisecond)
}

func cpuHog(dt time.Duration) {
	start := time.Now()
	for i := 0; ; i++ {
		if i%1000 == 0 && time.Since(start) > dt {
			return
		}
	}
}

func allocHog(dt time.Duration) {
	start := time.Now()
	var s [][]byte
	for i := 0; ; i++ {
		if i%1000 == 0 {
			if time.Since(start) > dt {
				return
			}

			w.start <- !t.failed || t.keepGoing
		}
		w := worklist[ended]
		dt := w.dt
		if t.lastHeading != dt.heading {
			t.lastHeading = dt.heading
			t.out(dt.heading)
		}
		if dt != last {
			// Assumes all the entries for a single dt are in one worklist.
			last = w.dt
			if vflag > 0 {
				fmt.Printf("# go tool dist test -run=^%s$\n", dt.name)
			}
		}
		if vflag > 1 {

	for _, summary := range summaries {
		basicGoroutineSummaryChecks(t, summary)
		hasSchedWaitTime = hasSchedWaitTime || summary.SchedWaitTime > 0
		if dt, ok := summary.BlockTimeByReason["sync"]; ok && dt > 0 {
			hasSyncBlockTime = true
		}
		if dt, ok := summary.RangeTime["GC mark assist"]; ok && dt > 0 {
			hasGCMarkAssistTime = true
		}
	}
	if !hasSchedWaitTime {
		t.Error("missing sched wait time")
	}
	if !hasSyncBlockTime {

        1 * timeSource.nanoTime() >> TimeUnit.MILLISECONDS.toNanos(timestamp)
    }

    private void setTime(int hours, int minutes, int seconds, int millis) {
        long dt = (hours * 3600 * 1000) + (minutes * 60 * 1000) + (seconds * 1000) + millis
        setTime(dt)
    }

#include "tensorflow/c/tf_datatype.h"

#include "tensorflow/core/framework/types.h"

size_t TF_DataTypeSize(TF_DataType dt) {
  return static_cast<size_t>(
      tensorflow::DataTypeSize(static_cast<tensorflow::DataType>(dt)));

}
.contentContainer .description dl dt, .contentContainer .details dl dt, .serializedFormContainer dl dt {
    font-size:1.1em;
    font-weight:bold;
    margin:10px 0 0 0;
    color:#4E4E4E;
}
.contentContainer .description dl dd, .contentContainer .details dl dd, .serializedFormContainer dl dd {
    margin:10px 0 10px 20px;
}
.serializedFormContainer dl.nameValue dt {
    margin-left:1px;
    font-size:1.1em;