case nopHeal:
				err = errSkipFile
			case SlashSeparator:
				res, err = healDiskFormat(ctx, objAPI, task.opts)
			default:
				if task.object == "" {
					res, err = objAPI.HealBucket(ctx, task.bucket, task.opts)
				} else {
					res, err = objAPI.HealObject(ctx, task.bucket, task.object, task.versionID, task.opts)
				}
			}

			if task.respCh != nil {
				task.respCh <- healResult{result: res, err: err}

shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to...

### Hardware attacks

Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other
running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running

	defer con.Close()
}

func TestNatsConnUserPass(t *testing.T) {
	opts := natsserver.DefaultTestOptions
	opts.Port = 14223
	opts.Username = "testminio"
	opts.Password = "miniotest"
	s := natsserver.RunServer(&opts)
	defer s.Shutdown()

	clientConfig := &NATSArgs{
		Enable: true,
		Address: xnet.Host{
			Name:      "localhost",
			Port:      (xnet.Port(opts.Port)),
			IsPortSet: true,
		},
		Subject:  "test",

			default:
			}

			sopts := speedTestOpts{
				objectSize:      opts.objectSize,
				concurrency:     concurrency,
				duration:        opts.duration,
				storageClass:    opts.storageClass,
				bucketName:      opts.bucketName,
				enableSha256:    opts.enableSha256,
				enableMultipart: opts.enableMultipart,
				creds:           opts.creds,
			}

			results := globalNotificationSys.SpeedTest(ctx, sopts)

				ServiceAccount: serviceAccount,
				Namespace:      ns,
				CloudrunAddr:   opts.IstiodAddr,
			}.ToStruct(),
			CertDir:            opts.CertDir,
			InsecureSkipVerify: opts.InsecureSkipVerify,
			XDSSAN:             opts.XDSSAN,
			GrpcOpts:           grpcOpts,
		},
	}, nil)
	if err != nil {
		return nil, fmt.Errorf("could not dial: %w", err)
	}
	err = adscConn.Run()
	if err != nil {

			// Enable custom socket send/recv buffers.
			if opts.SendBufSize > 0 {
				_ = unix.SetsockoptInt(fd, unix.SOL_SOCKET, unix.SO_SNDBUF, opts.SendBufSize)
			}

			if opts.RecvBufSize > 0 {
				_ = unix.SetsockoptInt(fd, unix.SOL_SOCKET, unix.SO_RCVBUF, opts.RecvBufSize)
			}

			if opts.NoDelay {
				_ = syscall.SetsockoptInt(fd, syscall.IPPROTO_TCP, unix.TCP_NODELAY, 1)

func NewFromFunc[T any](ttl time.Duration, opts Opts, update func(ctx context.Context) (T, error)) *Cache[T] {
	return &Cache[T]{
		ttl:      ttl,
		updateFn: update,
		opts:     opts,
	}
}

// InitOnce initializes the cache with a TTL and an update function. It is
// guaranteed to be called only once.
func (t *Cache[T]) InitOnce(ttl time.Duration, opts Opts, update func(ctx context.Context) (T, error)) {

		return
	}
	r.m.updateMeasurement(r.opts.BucketOptions, uint64(tokens))
	return
}

// NewMonitoredReader returns reference to a monitored reader that throttles reads to configured bandwidth for the
// bucket.
func NewMonitoredReader(ctx context.Context, m *Monitor, r io.Reader, opts *MonitorReaderOptions) *MonitoredReader {
	reader := MonitoredReader{
		r:        r,
		throttle: m.throttle(opts.BucketOptions),
		m:        m,

#include "tensorflow/core/platform/test.h"

TEST(CApiDebug, ScalarCPU) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* h = TestScalarTensorHandle(ctx, 1.0f);
  TFE_TensorDebugInfo* debug_info = TFE_TensorHandleTensorDebugInfo(h, status);