}

monitoring::GaugeCell<std::string>& SavedModelReadPath() {
  return *saved_model_read_path->GetCell();
}

monitoring::GaugeCell<std::string>& SavedModelReadPathAndSingleprint() {
  return *saved_model_read_path_and_singleprint->GetCell();
}

monitoring::GaugeCell<std::string>& SavedModelWriteFingerprint() {
  return *saved_model_write_fingerprint->GetCell();
}

  bool control_rets_updated_{false};
  monitoring::testing::CellReader<int64_t> mlir_function_pass_fallback_count_ =
      monitoring::testing::CellReader<int64_t>(
          /* metric name */
          "/tensorflow/core/mlir_function_pass_fallback_count");
  monitoring::testing::CellReader<int64_t>
      mlir_graph_optimization_pass_fallback_count_ =
          monitoring::testing::CellReader<int64_t>(

		monitoring.WithEnabled(enableStats),
	)

	xdsCacheSize = monitoring.NewGauge(
		"xds_cache_size",
		"Current size of xds cache",
		monitoring.WithEnabled(enableStats),
	)

	dependentConfigSize = monitoring.NewGauge(
		"xds_cache_dependent_config_size",
		"Current size of dependent configs",
		monitoring.WithEnabled(enableStats),
	)

#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/versions.pb.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/profile_utils/cpu_utils.h"
#include "tensorflow/core/platform/status.h"

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/monitoring/cell_reader.h"
#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"
#include "tensorflow/core/tpu/kernels/tpu_compile_op_support.h"
#include "tsl/lib/core/status_test_util.h"
#include "tsl/lib/monitoring/test_utils.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/client_library.h"
#include "xla/stream_executor/platform_manager.h"
#include "tensorflow/core/lib/monitoring/cell_reader.h"
#include "tensorflow/core/lib/monitoring/test_utils.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/config.pb.h"

	"istio.io/istio/pkg/kube/controllers"
	"istio.io/istio/pkg/kube/kclient"
	"istio.io/istio/pkg/log"
	"istio.io/istio/pkg/monitoring"
)

const (
	MultiClusterSecretLabel = "istio/multiCluster"
)

var (
	clusterLabel = monitoring.CreateLabel("cluster")
	timeouts     = monitoring.NewSum(
		"remote_cluster_sync_timeouts_total",

== Performance Monitoring

Gradle actively monitors heap usage to detect memory leaks in the Daemon.

When a memory leak exhausts available heap space, the Daemon:

. Finishes the currently running build.
. Restarts before running the next build.

Gradle enables this monitoring by default.

#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/errors.h"

#include "xla/mlir_hlo/mhlo/utils/type_conversion.h"
#include "tensorflow/core/lib/monitoring/counter.h"

namespace mlir {
namespace mhlo {
namespace {

#define GEN_PASS_DEF_LEGALIZETF
#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_tf_passes.h.inc"

auto *mlir_legalization_count = tensorflow::monitoring::Counter<1>::New(
    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_count",