"type": "object",
      "properties": {
        "annotations": {
          "description": "Annotations is an unstructured key value map stored with a resource that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations",

// SigContext will be zero.
//
// Buf is where the traceback information should be stored. It should
// be PC values, such that Buf[0] is the PC of the caller, Buf[1] is
// the PC of that function's caller, and so on.  Max is the maximum
// number of entries to store.  The function should store a zero to
// indicate the top of the stack, or that the caller is on a different
// stack, presumably a Go stack.

}

var dwtypes dwarf.DWDie

// newattr attaches a new attribute to the specified DIE.
//
// FIXME: at the moment attributes are stored in a linked list in a
// fairly space-inefficient way -- it might be better to instead look
// up all attrs in a single large table, then store indices into the
// table in the DIE. This would allow us to common up storage for
// attributes that are shared by many DIEs (ex: byte size of N).

	return p
}

func addPods(store cache.Store, namespace string, nPods int, nPorts int, nNotReady int, ipFamilies []v1.IPFamily) {
	for i := 0; i < nPods+nNotReady; i++ {
		isReady := i < nPods
		pod := testPod(namespace, i, nPorts, isReady, ipFamilies)
		store.Add(pod)
	}
}

        auto const_loc = TensorLoc(const_tensor, builder, base_loc);
        StatusOr<Operation*> op_or_err;
        std::vector<uint8_t> buffer;
        // Check if constant tensor is stored outside of the flatbuffers.
        if (IsValidBufferOffset(buffers[const_tensor.buffer]->offset)) {
          const uint8_t* file_begin_ptr =
              reinterpret_cast<const uint8_t*>(model_ptr->allocation()->base());

		if err != nil {
			log.Errorf("create envoyfilter failed %v", err)
		}
	}
	return store
}

func buildEnvoyFilterConfigStoreWithPriorities(configPatches []*networking.EnvoyFilter_EnvoyConfigObjectPatch, priorities []int32) model.ConfigStore {
	store := memory.Make(collections.Pilot)

	for i, cp := range configPatches {
		_, err := store.Create(config.Config{
			Meta: config.Meta{

	return v.AuxInt == 0
}

// auxFrom64F encodes a float64 value so it can be stored in an AuxInt.
func auxFrom64F(f float64) int64 {
	if f != f {
		panic("can't encode a NaN in AuxInt field")
	}
	return int64(math.Float64bits(f))
}

// auxFrom32F encodes a float32 value so it can be stored in an AuxInt.
func auxFrom32F(f float32) int64 {
	if f != f {
		panic("can't encode a NaN in AuxInt field")

            "additionalProperties": {
              "default": "",
              "type": "string"
            },
            "description": "Annotations is an unstructured key value map stored with a resource that may be set by external tools to store and retrieve arbitrary metadata. They are not queryable and should be preserved when modifying objects. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations",

 * H  = X2*T1
 * H  = H-U1
 * Z3 = Z1*Z2
 * Z3 = Z3*H << store-out Z3 result reg.. could override Z1, if slices have same backing array
 *
 * S1 = Z2*T2
 * S1 = Y1*S1
 * R  = Z1*T1
 * R  = Y2*R
 * R  = R-S1
 *
 * T1 = H*H
 * T2 = H*T1
 * U1 = U1*T1
 *
 * X3 = R*R
 * X3 = X3-T2
 * T1 = 2*U1
 * X3 = X3-T1 << store-out X3 result reg
 *
 * T2 = S1*T2
 * Y3 = U1-X3

		}
		funcLookupCache.m.Store(hash, append(rts, tt))
		return toType(tt)
	}

	// Look in known types for the same string representation.
	str := funcStr(ft)
	for _, tt := range typesByString(str) {
		if haveIdenticalUnderlyingType(&ft.Type, tt, true) {
			return addToCache(tt)
		}
	}

	// Populate the remaining fields of ft and store in cache.