return cfile.WriteCounters(w)
}

// ClearCounters clears/resets all coverage counter variables in the
// currently running program. It returns an error if the program in
// question was not built with the "-cover" flag. Clearing of coverage
// counters is also not supported for programs not using atomic
// counter mode (see more detailed comments below for the rationale
// here).
func ClearCounters() error {

    "/tensorflow/core/saved_model/write/count",
    "The number of SavedModels successfully written.", "write_version");

// Counter that tracks total number and `write_version` of SavedModels read.
auto* saved_model_read_counter = monitoring::Counter<1>::New(
    "/tensorflow/core/saved_model/read/count",
    "The number of SavedModels successfully loaded.", "write_version");

But as you go away from the counter and sit at the table with a number for your turn, you can switch 🔀 your attention to your crush, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" as is flirting with your crush 😍.

	} else {
		got := fmt.Sprintf("%v", err)
		checkWant("meta data", got)
	}

	// Now try to emit counter data file to a bad dir.
	if err := coverage.WriteCountersDir(mangled); err == nil {
		log.Fatal("expected error emitting counter data to bad dir")
	} else {
		got := fmt.Sprintf("%v", err)
		checkWant("counter data", got)
	}
}

func emitToUnwritableDir() {
	log.SetPrefix("emitToUnwritableDir: ")

	// This is an action which might be taken on a pod failure - the counter towards
	// .backoffLimit, represented by the job's .status.failed field, is not
	// incremented and a replacement pod is created.
	PodFailurePolicyActionIgnore PodFailurePolicyAction = "Ignore"

	// This is an action which might be taken on a pod failure - the pod failure
	// is handled in the default way - the counter towards .backoffLimit,

namespace tensorflow {
namespace {

auto* load_attempt_count = monitoring::Counter<2>::New(
    "/tensorflow/cc/saved_model/load_attempt_count",
    "The number of times a SavedModel was successfully loaded.", "model_path",
    "status");
auto* load_latency = monitoring::Counter<1>::New(
    "/tensorflow/cc/saved_model/load_latency",

	// Read counter data files.
	for k, cdf := range p.CounterDataFiles {
		cf, err := os.Open(cdf)
		if err != nil {
			return r.fatal("opening counter data file %s: %s", cdf, err)
		}
		defer func(f *os.File) {
			f.Close()
		}(cf)
		var mr *MReader
		mr, err = NewMreader(cf)
		if err != nil {
			return r.fatal("creating reader for counter data file %s: %s", cdf, err)
		}

void GroupByDialectPass::processFunction(
    mlir::func::FuncOp func, int& counter,
    llvm::SmallDenseSet<StringRef>& dialects, Operation* module) {
  // don't re-process functions we generated
  if (func->getAttr("dialect")) return;
  processRegion(func.getBody(), counter, dialects, module);
}

void GroupByDialectPass::processRegion(mlir::Region& region, int& counter,

my_metric{} 0
# TYPE my_other_metric counter
my_other_metric{} 0
# TYPE my_metric counter
my_metric{} 0
`,
			expectParseError: true,
		},
		{
			name: "conflict metric labeled",
			envoy: `# TYPE my_metric counter
my_metric{app="foo"} 0
`,
			app: `# TYPE my_metric counter
my_metric{app="bar"} 0
`,
			output: `# TYPE my_metric counter
my_metric{app="foo"} 0
# TYPE my_metric counter

     *
     * GIDs generated this way will be in the form of
     * {@code <class-name-hash-32> <obj-hash-32> <counter-32>}
     */
    public String generateGid(String pbxClassName, int hash) {
        int counter = 0;
        String gid;
        do {
            gid = String.format("%08X%08X%08X", pbxClassName.hashCode(), hash, counter++);
        } while (generatedAndReservedIds.contains(gid));
        generatedAndReservedIds.add(gid);