// note that the name, namespace, and port are required and by default all
// created addresses use HTTPS scheme.
// for example:
//
//	name=ross namespace=andromeda resolves to https://ross.andromeda.svc:443
func (sr defaultServiceResolver) ResolveEndpoint(namespace, name string, port int32) (*url.URL, error) {
	if len(name) == 0 || len(namespace) == 0 || port == 0 {

## Local testing

To run the fuzzers, follow these steps:

```bash
git clone --depth=1 https://github.com/google/oss-fuzz.git
cd oss-fuzz
python infra/helper.py build_image istio
python infra/helper.py build_fuzzers istio ~/go/src/istio.io/istio
```

Reproduce failure:

```bash

//    IEEE     +-1.07e9      30000      2.9e-16     8.1e-17
//
// Partial loss of accuracy begins to occur at x = 2**30 = 1.074e9.  The loss
// is not gradual, but jumps suddenly to about 1 part in 10e7.  Results may
// be meaningless for x > 2**49 = 5.6e14.
// [Accuracy loss statement from sin.go comments.]
//
// Cephes Math Library Release 2.8:  June, 2000

inverseRules:
  - selectorRegexp: k8s[.]io
    allowedPrefixes:

inverseRules:
  - selectorRegexp: k8s[.]io
    forbiddenPrefixes:
      - k8s.io/kubernetes/cmd/import-boss/testdata/transitive/aaa

#define CSIGSTKSZ 0x4000
#else
#define CSIGSTKSZ SIGSTKSZ
#endif

static stack_t oss;
static char signalStack[CSIGSTKSZ];

static void changeSignalStack(void) {
	stack_t ss;
	memset(&ss, 0, sizeof ss);
	ss.ss_sp = signalStack;
	ss.ss_flags = 0;
	ss.ss_size = CSIGSTKSZ;
	if (sigaltstack(&ss, &oss) < 0) {
		perror("sigaltstack");
		abort();
	}
}

static void restoreSignalStack(void) {

inverseRules:
  - selectorRegexp: k8s[.]io
    forbiddenPrefixes:

inverseRules:
  - selectorRegexp: k8s[.]io
    allowedPrefixes:
      - k8s.io/kubernetes/cmd/import-boss/testdata/transitive/aaa

// compiledir

// Copyright 2015 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Issue 12677: Type loss during export/import of inlined function body.

// Lowers SoftmaxCrossEntropyWithLogitsOp using simpler TensorFlow ops. The op
// computes loss and backprop of the loss with respect to 'features'.
//
// Softmax cross entropy loss is defined as follows:
//
//  loss = Sum(-labels * Log(Exp(features) / Sum(Exp(features)))
//  loss = Sum(-labels * LogSoftmax(features))
//
// Computing gradient of the loss with respect to features gives us,
//