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]

	v_0 := v.Args[0]
	b := v.Block
	typ := &b.Func.Config.Types
	// match: (AtomicStore32 ptr val mem)
	// result: (Select1 (XCHGL <types.NewTuple(typ.UInt32,types.TypeMem)> val ptr mem))
	for {
		ptr := v_0
		val := v_1
		mem := v_2
		v.reset(OpSelect1)
		v0 := b.NewValue0(v.Pos, OpAMD64XCHGL, types.NewTuple(typ.UInt32, types.TypeMem))
		v0.AddArg3(val, ptr, mem)
		v.AddArg(v0)

                                    Type identifies the type of filter to apply. As with other API fields,
                                    types are classified into three conformance levels:


                                    - Core: Filter types and their corresponding configuration defined by
                                      "Support: Core" in this package, e.g. "RequestHeaderModifier". All

		v.AuxInt = int64ToAuxInt(int64(c))
		v.Aux = typeToAux(types.Types[types.TUINT8])
		v.AddArg2(sptr, mem)
		return true
	}
	// match: (SelectN [0] call:(StaticCall {sym} sptr (Const32 [c]) mem))
	// cond: isInlinableMemclr(config, int64(c)) && isSameCall(sym, "runtime.memclrNoHeapPointers") && call.Uses == 1 && clobber(call)
	// result: (Zero {types.Types[types.TUINT8]} [int64(c)] sptr mem)
	for {

        tf.dtypes.QUANTIZED_DTYPES.
    *   Update sklearn imports for deprecated packages.
    *   Deprecate `Variable.count_up_to` and `tf.count_up_to` in favor of
        `Dataset.range`.
    *   Export `confusion_matrix` op as `tf.math.confusion_matrix` instead of
        `tf.train.confusion_matrix`.
    *   Add `tf.dtypes.` endpoint for every constant in dtypes.py. Moving

            "type": "string"
          },
          "metadata": {
            "allOf": [
              {
                "$ref": "#/components/schemas/io.k8s.apimachinery.pkg.apis.meta.v1.ObjectMeta"

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

            "description": "fsGroupChangePolicy defines behavior of changing ownership and permission of the volume before being exposed inside Pod. This field will only apply to volume types which support fsGroup based ownership(and permissions). It will have no effect on ephemeral volume types such as: secret, configmaps and emptydir. Valid values are \"OnRootMismatch\" and \"Always\". If not specified, \"Always\" is used. Note that this field cannot be set when spec.os.name is windows.",...

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)

        }
        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
      }
      op {
        name: "SaveV2"