- Updated `kubedns` and `nodelocaldns` to release version `1.22.28`. ([#121908](https://github.com/kubernetes/kubernetes/pull/121908), [@mzaian](https://github.com/mzaian))

"grid-rows",["gridTemplateRows"]]]),flexDirection:({addUtilities:i})=>{i({".flex-row":{"flex-direction":"row"},".flex-row-reverse":{"flex-direction":"row-reverse"},".flex-col":{"flex-direction":"column"},".flex-col-reverse":{"flex-direction":"column-reverse"}})},flexWrap:({addUtilities:i})=>{i({".flex-wrap":{"flex-wrap":"wrap"},".flex-wrap-reverse":{"flex-wrap":"wrap-reverse"},".flex-nowrap":{"flex-wrap":"nowrap"}})},placeContent:({addUtilities:i})=>{i({".place-content-center":{"place-content":"...

  %strides = "tf.Const"() {value = dense<[1, 4, -1]> : tensor<3xi32>} : () -> (tensor<3xi32>)

  // CHECK: %[[REVERSE:.*]] = "mhlo.reverse"(%[[INPUT]])

  // CHECK: %[[SLICE:.*]] = "mhlo.slice"(%[[REVERSE]])
  // CHECK-DAG-SAME: limit_indices = dense<[4, 65, 1024]>
  // CHECK-DAG-SAME: start_indices = dense<[0, 4, 2]>
  // CHECK-DAG-SAME: strides = dense<[1, 4, 1]>

  %res_m4 = "tfl.cumsum"(%arg, %axis_m4) {exclusive = false, reverse = false} : (tensor<1x2x1x3xf32>, tensor<i32>) -> tensor<1x2x1x3xf32>  // Eliminated
  %res_m3 = "tfl.cumsum"(%arg, %axis_m3) {exclusive = false, reverse = false} : (tensor<1x2x1x3xf32>, tensor<i32>) -> tensor<1x2x1x3xf32>
  %res_m2 = "tfl.cumsum"(%arg, %axis_m2) {exclusive = false, reverse = false} : (tensor<1x2x1x3xf32>, tensor<i32>) -> tensor<1x2x1x3xf32>  // Eliminated

	{BRD, 0xfc0007fe00000000, 0x7c00017600000000, 0xf80100000000, // Byte-Reverse Doubleword X-form (brd RA,RS)
		[6]*argField{ap_Reg_11_15, ap_Reg_6_10}},
	{BRH, 0xfc0007fe00000000, 0x7c0001b600000000, 0xf80100000000, // Byte-Reverse Halfword X-form (brh RA,RS)
		[6]*argField{ap_Reg_11_15, ap_Reg_6_10}},
	{BRW, 0xfc0007fe00000000, 0x7c00013600000000, 0xf80100000000, // Byte-Reverse Word X-form (brw RA,RS)
		[6]*argField{ap_Reg_11_15, ap_Reg_6_10}},

    // negative.
    int64_t axis = (*axis_attr.begin()).getSExtValue();
    if (axis < 0) {
      axis += rank;
    }

    // If we're supposed to sum things up in the reverse direction, we reverse
    // the input and then later reverse the output.
    if (op.getReverse()) {
      llvm::SmallVector<int64_t, 4> dims_to_reverse({axis});
      input = rewriter.create<ReverseOp>(

  %214 = "tf.Tile"(%213, %211) {device = ""} : (tensor<1xi64>, tensor<1xi64>) -> tensor<?xi64>
  %215 = "tf.Cumsum"(%214, %14) {device = "", exclusive = false, reverse = false} : (tensor<?xi64>, tensor<i32>) -> tensor<?xi64>
  %216 = "tf.ConcatV2"(%11, %215, %3) {device = ""} : (tensor<1xi64>, tensor<?xi64>, tensor<i32>) -> tensor<?xi64>

tems:flex-start}.uk-flex-middle{align-items:center}.uk-flex-bottom{align-items:flex-end}.uk-flex-row{flex-direction:row}.uk-flex-row-reverse{flex-direction:row-reverse}.uk-flex-column{flex-direction:column}.uk-flex-column-reverse{flex-direction:column-reverse}.uk-flex-nowrap{flex-wrap:nowrap}.uk-flex-wrap{flex-wrap:wrap}.uk-flex-wrap-reverse{flex-wrap:wrap-reverse}.uk-flex-wrap-stretch{align-content:stretch}.uk-flex-wrap-top{align-content:flex-start}.uk-flex-wrap-middle{align-content:center}.uk-...

	instrumentMemory    bool // whether to instrument memory operations

	// If doing open-coded defers, list of info about the defer calls in
	// scanning order. Hence, at exit we should run these defers in reverse
	// order of this list
	openDefers []*openDeferInfo
	// For open-coded defers, this is the beginning and end blocks of the last
	// defer exit code that we have generated so far. We use these to share

// CHECK-DAG:       %[[VAL_2:.*]] = "tf.Const"() <{value = dense<0> : tensor<i64>}> : () -> tensor<i64>
// CHECK:           %[[VAL_3:.*]] = "tf.Cumsum"(%[[VAL_0]], %[[VAL_2]]) <{exclusive = false, reverse = false}> : (tensor<4x12xf32>, tensor<i64>) -> tensor<4x12xf32>
// CHECK:           return %[[VAL_3]] : tensor<4x12xf32>
// CHECK:         }
func.func @lowered_cumsum(%arg0: tensor<4x12xf32>) -> tensor<4x12xf32> {