v_2 := v.Args[2]
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	b := v.Block
	typ := &b.Func.Config.Types
	// match: (ADCSflags x y (Select1 <types.TypeFlags> (ADDSconstflags [-1] (ADCzerocarry <typ.UInt64> c))))
	// result: (ADCSflags x y c)
	for {
		x := v_0
		y := v_1
		if v_2.Op != OpSelect1 || v_2.Type != types.TypeFlags {
			break
		}
		v_2_0 := v_2.Args[0]

	// result: (Select0 <t> (BLSRL x))
	for {
		t := v.Type
		for _i0 := 0; _i0 <= 1; _i0, v_0, v_1 = _i0+1, v_1, v_0 {
			x := v_0
			if v_1.Op != OpAMD64ADDLconst || auxIntToInt32(v_1.AuxInt) != -1 || x != v_1.Args[0] || !(buildcfg.GOAMD64 >= 3) {
				continue
			}
			v.reset(OpSelect0)
			v.Type = t
			v0 := b.NewValue0(v.Pos, OpAMD64BLSRL, types.NewTuple(typ.UInt32, types.TypeFlags))
			v0.AddArg(x)
			v.AddArg(v0)

                                  type: object
                              type: object
                              x-kubernetes-map-type: atomic
                          type: object
                      type: object
                    hostname:
                      description: |-
                        Hostname specifies the virtual hostname to match for protocol types that

	b := v.Block
	config := b.Func.Config
	typ := &b.Func.Config.Types
	// match: (ConstSlice)
	// cond: config.PtrSize == 4
	// result: (SliceMake (ConstNil <v.Type.Elem().PtrTo()>) (Const32 <typ.Int> [0]) (Const32 <typ.Int> [0]))
	for {
		if !(config.PtrSize == 4) {
			break
		}
		v.reset(OpSliceMake)
		v0 := b.NewValue0(v.Pos, OpConstNil, v.Type.Elem().PtrTo())
		v1 := b.NewValue0(v.Pos, OpConst32, typ.Int)

* Making the `tf.function` type system fully available:

    * `tf.types.experimental.TraceType` now allows custom tf.function inputs to declare Tensor decomposition and type casting support.

            "description": "RollingUpdate is used to communicate parameters when Type is RollingUpdateStatefulSetStrategyType."
          },
          "type": {
            "description": "Type indicates the type of the StatefulSetUpdateStrategy. Default is RollingUpdate.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "io.k8s.api.autoscaling.v1.Scale": {

from tensorflow.python.ops import bitwise_ops

# flip 2 (00000010) to -3 (11111101)
tf.assert_equal(-3, bitwise_ops.invert(2))

dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
              dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]

inputs = [0, 5, 3, 14]
for dtype in dtype_list:
  # Because of issues with negative numbers, let's test this indirectly.
  # 1. invert(a) and a = 0

Class <org.gradle.internal.build.event.types.AbstractOperationResult> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (AbstractOperationResult.java:0)
Class <org.gradle.internal.build.event.types.AbstractProgressEvent> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (AbstractProgressEvent.java:0)

### General ephemeral volumes

          type: DT_STRING
        }
        input_arg {
          name: "shape_and_slices"
          type: DT_STRING
        }
        output_arg {
          name: "tensors"
          type_list_attr: "dtypes"
        }
        attr {
          name: "dtypes"
          type: "list(type)"
          has_minimum: true
          minimum: 1
        }
        is_stateful: true