namespace tensorflow {
namespace calibrator {

using ::stablehlo::quantization::CalibrationOptions;

// AverageMinMax calibration calculates the average of min and max values.
// average of min = sum of min values / number of samples
// average of max = sum of max values / number of samples
class CalibrationStatisticsCollectorAverageMinMax
    : public CalibrationStatisticsCollectorBase {
 public:

var (
	replicationAverageActiveWorkersMD = NewGaugeMD(replicationAverageActiveWorkers,
		"Average number of active replication workers")
	replicationAverageQueuedBytesMD = NewGaugeMD(replicationAverageQueuedBytes,
		"Average number of bytes queued for replication since server start")
	replicationAverageQueuedCountMD = NewGaugeMD(replicationAverageQueuedCount,
		"Average number of objects queued for replication since server start")

	Stop()
	size := buf.Len()
	if size == 0 {
		t.Fatalf("trace is empty")
	}
	time.Sleep(100 * time.Millisecond)
	if size != buf.Len() {
		t.Fatalf("trace writes after stop: %v -> %v", size, buf.Len())
	}
	saveTrace(t, buf, "TestTraceStartStop")
}

func TestTraceDoubleStart(t *testing.T) {
	if IsEnabled() {
		t.Skip("skipping because -test.trace is set")
	}
	Stop()
	buf := new(bytes.Buffer)

| `minio_system_cpu_avg_idle`   | `gauge` | Average CPU idle time              | `server` |
| `minio_system_cpu_avg_iowait` | `gauge` | Average CPU IOWait time            | `server` |
| `minio_system_cpu_load`       | `gauge` | CPU load average 1min              | `server` |
| `minio_system_cpu_load_perc`  | `gauge` | CPU load average 1min (percentage) | `server` |

StringAttr GetAvgPoolOpPadAttr(Builder& builder, mhlo::CompositeOp op);

// Get dense attr for a matrix that corrects the over counting of divisors when
// casting an average pool with ceil mode on in terms of average pool with it
// off.
DenseFPElementsAttr GetCorrectionMatrix(Builder& builder, mhlo::CompositeOp op);

}  // namespace odml
}  // namespace mlir

  optional string name = 1;

  // targetAverageUtilization is the target value of the average of the
  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.
  // +optional
  optional int32 targetAverageUtilization = 2;

  // targetAverageValue is the target value of the average of the
  // resource metric across all relevant pods, as a raw value (instead of as

	"targetAverageUtilization": "targetAverageUtilization is the target value of the average of the resource metric across all relevant pods, represented as a percentage of the requested value of the resource for the pods.",

  optional string name = 1;

  // targetAverageUtilization is the target value of the average of the
  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.
  // +optional
  optional int32 targetAverageUtilization = 2;

  // targetAverageValue is the target value of the average of the
  // resource metric across all relevant pods, as a raw value (instead of as

  optional string name = 1;

  // targetAverageUtilization is the target value of the average of the
  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.
  // +optional
  optional int32 targetAverageUtilization = 2;

  // targetAverageValue is the target value of the average of the
  // resource metric across all relevant pods, as a raw value (instead of as

  // for any `x`, `ceil(x) - floor(x) <= 1`.

  // Consider that we pad the input of a average pool with floor rounding to the
  // appropriate size and switch the rounding mode to ceil. When computing the
  // average of a given window, the elements which exist in the newly padded
  // zones will be counted as present elements. Therefore in some windows we