- Improved scheduling latency when many gated pods ([#124851](https://github.com/kubernetes/kubernetes/pull/124851), [@gabesaba](https://github.com/gabesaba)) [SIG Scheduling and Testing]
- Kube-apiserver: fixes a 1.28 regression printing pods with invalid initContainer status ([#124910](https://github.com/kubernetes/kubernetes/pull/124910), [@liggitt](https://github.com/liggitt)) [SIG Node]

	case n < 125:
		return uint16(idnaValues[n<<6+uint32(b)])
	default:
		n -= 125
		return uint16(idnaSparse.lookup(n, b))
	}
}

// idnaValues: 127 blocks, 8128 entries, 16256 bytes
// The third block is the zero block.
var idnaValues = [8128]uint16{
	// Block 0x0, offset 0x0
	0x00: 0x0080, 0x01: 0x0080, 0x02: 0x0080, 0x03: 0x0080, 0x04: 0x0080, 0x05: 0x0080,

	case n < 125:
		return uint16(idnaValues[n<<6+uint32(b)])
	default:
		n -= 125
		return uint16(idnaSparse.lookup(n, b))
	}
}

// idnaValues: 127 blocks, 8128 entries, 16256 bytes
// The third block is the zero block.
var idnaValues = [8128]uint16{
	// Block 0x0, offset 0x0
	0x00: 0x0080, 0x01: 0x0080, 0x02: 0x0080, 0x03: 0x0080, 0x04: 0x0080, 0x05: 0x0080,

	case n < 125:
		return uint16(idnaValues[n<<6+uint32(b)])
	default:
		n -= 125
		return uint16(idnaSparse.lookup(n, b))
	}
}

// idnaValues: 127 blocks, 8128 entries, 16256 bytes
// The third block is the zero block.
var idnaValues = [8128]uint16{
	// Block 0x0, offset 0x0
	0x00: 0x0080, 0x01: 0x0080, 0x02: 0x0080, 0x03: 0x0080, 0x04: 0x0080, 0x05: 0x0080,

	// result: (DUFFCOPY [8 * (128 - s/4)] dst src mem)
	for {
		s := auxIntToInt64(v.AuxInt)
		t := auxToType(v.Aux)
		dst := v_0
		src := v_1
		mem := v_2
		if !(s%4 == 0 && s > 4 && s <= 512 && t.Alignment()%4 == 0 && !config.noDuffDevice && logLargeCopy(v, s)) {
			break
		}
		v.reset(OpARMDUFFCOPY)
		v.AuxInt = int64ToAuxInt(8 * (128 - s/4))
		v.AddArg3(dst, src, mem)

         <match value="\n@article{" type="stringignorecase" offset="2:128"/>
         <match value="\n@book{" type="stringignorecase" offset="2:128"/>
         <match value="\n@inbook{" type="stringignorecase" offset="2:128"/>
         <match value="\n@incollection{" type="stringignorecase" offset="2:128"/>
         <match value="\n@inproceedings{" type="stringignorecase" offset="2:128"/>

	// cond: s > 16 && s <= 4*128 && s%4 == 0 && !config.noDuffDevice
	// result: (DUFFZERO [1*(128-s/4)] destptr (MOVLconst [0]) mem)
	for {
		s := auxIntToInt64(v.AuxInt)
		destptr := v_0
		mem := v_1
		if !(s > 16 && s <= 4*128 && s%4 == 0 && !config.noDuffDevice) {
			break
		}
		v.reset(Op386DUFFZERO)
		v.AuxInt = int64ToAuxInt(1 * (128 - s/4))

	/*8112*/ uint16(xArgR64),
	/*8113*/ uint16(xArgRM64),
	/*8114*/ uint16(xMatch),
	/*8115*/ uint16(xSetOp), uint16(ADC),
	/*8117*/ uint16(xReadIb),
	/*8118*/ uint16(xArgAL),
	/*8119*/ uint16(xArgImm8u),
	/*8120*/ uint16(xMatch),
	/*8121*/ uint16(xCondIs64), 8124, 8140,
	/*8124*/ uint16(xCondDataSize), 8128, 8134, 0,
	/*8128*/ uint16(xSetOp), uint16(ADC),
	/*8130*/ uint16(xReadIw),
	/*8131*/ uint16(xArgAX),

// CHECK-LABEL: fuseTileWithBinaryOp1
func.func @fuseTileWithBinaryOp1(%arg0: tensor<1x1xf32>, %arg1: tensor<1x128xf32>) -> tensor<1x128xf32> {
  %cst_0 = arith.constant dense<1.0> : tensor<f32>
  %cst_1 = arith.constant dense<[1, 128]> : tensor<2xi32>
  %0 = "tfl.add"(%arg0, %cst_0) {fused_activation_function = "NONE"} : (tensor<1x1xf32>, tensor<f32>) -> tensor<1x1xf32>
  %1 = "tfl.sqrt"(%0) : (tensor<1x1xf32>) -> tensor<1x1xf32>

// CHECK:           %[[VAL_4:.*]] = arith.constant dense<[0, 1]> : tensor<2xi64>
// CHECK:           %[[VAL_5:.*]] = arith.constant dense<[7, 128]> : tensor<2xi64>
// CHECK:           %[[VAL_6:.*]] = "tf.Slice"(%[[VAL_3]], %[[VAL_4]], %[[VAL_5]]) : (tensor<8x129xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor<7x128xf32>
// CHECK:           return %[[VAL_6]] : tensor<7x128xf32>