//     specific system recommendations based on current support case statistics and storage performance possibilities.
//     Vendors might change the settings based on the configuration and scale out of the solution (more storage nodes =>
//     higher task limit).
//
//     <S3ConcurrentTaskLimit>
//

	data   int64
	udata  *byte
	ext    [4]uint64
}

type bintime struct {
	sec  int32
	frac uint64
}

type vdsoTimehands struct {
	algo         uint32
	gen          uint32
	scale        uint64
	offset_count uint32
	counter_mask uint32
	offset       bintime
	boottime     bintime
	x86_shift    uint32
	x86_hpet_idx uint32
	res          [6]uint32
}

type vdsoTimekeep struct {

	data   int64
	udata  *byte
	ext    [4]uint64
}

type bintime struct {
	sec  int64
	frac uint64
}

type vdsoTimehands struct {
	algo         uint32
	gen          uint32
	scale        uint64
	offset_count uint32
	counter_mask uint32
	offset       bintime
	boottime     bintime
	physical     uint32
	res          [7]uint32
}

type vdsoTimekeep struct {
	ver       uint32

	for _, successCase := range successCases {
		if errs := ValidateScale(&successCase); len(errs) != 0 {
			t.Errorf("expected success: %v", errs)
		}
	}

	errorCases := []struct {
		scale autoscaling.Scale
		msg   string
	}{{
		scale: autoscaling.Scale{
			ObjectMeta: metav1.ObjectMeta{
				Name:      "frontend",
				Namespace: metav1.NamespaceDefault,
			},
			Spec: autoscaling.ScaleSpec{
				Replicas: -1,
			},
		},

        auto shape = GetAsVector(quant_tensor->shape());
        if (kUseUpdatedHybridSchemeDefault) {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), shape[0]);
        } else {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), 1);
        }
      } else {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT32);
      }
    }

		argLen:         2,
		hasSideEffects: true,
		generic:        true,
	},
}

func (o Op) Asm() obj.As          { return opcodeTable[o].asm }
func (o Op) Scale() int16         { return int16(opcodeTable[o].scale) }
func (o Op) String() string       { return opcodeTable[o].name }
func (o Op) SymEffect() SymEffect { return opcodeTable[o].symEffect }

		a.writeNameTo(w, abiDetail)

	case TYPE_MEM:
		a.WriteNameTo(w)
		if a.Index != REG_NONE {
			if a.Scale == 0 {
				// arm64 shifted or extended register offset, scale = 0.
				fmt.Fprintf(w, "(%v)", Rconv(int(a.Index)))
			} else {
				fmt.Fprintf(w, "(%v*%d)", Rconv(int(a.Index)), int(a.Scale))
			}
		}

	case TYPE_CONST:
		io.WriteString(w, "$")
		a.WriteNameTo(w)
		if a.Reg != 0 {

            "type": "string"
          },
          "replicas": {

}

TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale,
                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,

//              scale * rsqrt(variance + epsilon)
// CHECK:  %[[MUL1:.*]] = "tf.Mul"(%[[ARG1:.*]], %[[RSQRT]])
//              x * scale * rsqrt(variance + epsilon)
// CHECK:  %[[MUL2:.*]] = "tf.Mul"(%[[ARG0:.*]], %[[MUL1]])
//              mean * scale * rsqrt(variance + epsilon)
// CHECK:  %[[MUL3:.*]] = "tf.Mul"(%[[ARG3:.*]], %[[MUL1]])
//              offset - mean * scale * rsqrt(variance + epsilon)