llvm::cl::Optional, llvm::cl::init("serve"));

// NOLINTNEXTLINE
opt<bool> elide_large_elements_attrs(
    "e",
    llvm::cl::desc(
        "Elide large elements attrs while dumping the output StableHLO."),
    llvm::cl::Optional, llvm::cl::init(false));

}  // namespace

namespace mlir {

namespace {
// Dump the ModuleOp 'module' to the file specified using 'outputFileName'

    ): KaCompilationResult
}

public typealias KtCompilerFacility = KaCompilerFacility

public interface KaCompilerFacilityMixIn : KaSessionMixIn {
    /**
     * Compile the given [file] in-memory (without dumping the compiled binaries to a disk).
     *
     * @param file A file to compile.
     *  The file must be either a source module file, or a [KtCodeFragment].

// in the serialized operation on which the pass is invoked. For verbose log
// level >= 1, `bridge_logger.cc` prints details about pass invocations for
// which the IR dumping was skipped because of a filter.
class BridgeLoggerConfig : public mlir::PassManager::IRPrinterConfig {
 public:
  explicit BridgeLoggerConfig(
      bool print_module_scope = false, bool print_after_only_on_change = true,

    incoming_headers = [
        # All applications should propagate x-request-id. This header is
        # included in access log statements and is used for consistent trace
        # sampling and log sampling decisions in Istio.
        'x-request-id',

        # Lightstep tracing header. Propagate this if you use lightstep tracing
        # in Istio (see

namespace tensorflow {

inline constexpr absl::string_view kCrashReproducerStdErr = "-";
inline constexpr absl::string_view kCrashReproducerCrashAnalysis =
    "crash_analysis";

// Creates a file to use for dumping and returns success if a file could be
// created. The opened file is placed in 'os' and the path of the file used is
// placed in 'filepath'.
//
// If the TF_DUMP_GRAPH_PREFIX environment variable is kCrashReproducerStdErr,

			}
		})
	}
	b.build()
	return nil
}

// scaleHeapSample adjusts the data from a heap Sample to
// account for its probability of appearing in the collected
// data. heap profiles are a sampling of the memory allocations
// requests in a program. We estimate the unsampled value by dividing
// each collected sample by its probability of appearing in the
// profile. heap profiles rely on a poisson process to determine

		tproxy           *model.Proxy
		envPilotSampling float64
	}{
		{
			name:             "random-sampling-env",
			tproxy:           getProxy(),
			envPilotSampling: 80.0,
			in: &meshconfig.Tracing{
				Tracer:           nil,
				CustomTags:       nil,
				MaxPathTagLength: 0,
				Sampling:         0,
			},
			out: &hcm.HttpConnectionManager_Tracing{
				MaxPathTagLength: nil,

		return
	}
	st := time.Now()
	defer func() {
		l := scopes.Framework.Debugf
		if time.Since(st) > time.Second*10 {
			// Log slow dumps at higher level
			l = scopes.Framework.Infof
		}
		l("Done dumping: %s for %s (%v)", s.id, ctx.ID(), time.Since(st))
	}()
	s.mu.Lock()
	defer s.mu.Unlock()
	if recursive {
		for _, c := range s.children {
			c.dump(ctx, recursive)
		}
	}
	wg := sync.WaitGroup{}

//
// Each Profile has a unique name. A few profiles are predefined:
//
//	goroutine    - stack traces of all current goroutines
//	heap         - a sampling of memory allocations of live objects
//	allocs       - a sampling of all past memory allocations
//	threadcreate - stack traces that led to the creation of new OS threads
//	block        - stack traces that led to blocking on synchronization primitives

// Render plot progressively so rough outline is visible quickly even
// for very complex MUTs. Start by computing just a few window sizes
// and then add more window sizes.
//
// Consider using sampling to compute an approximate MUT. This would
// work by sampling the mutator utilization at randomly selected
// points in time in the trace to build an empirical distribution. We
// could potentially put confidence intervals on these estimates and