St=["background","cite","href","itemtype","longdesc","poster","src","xlink:href"],Nt=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Dt=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i;function At(t,e,n){if(0===t.length)return t;if(n&&"function"==typeof n)return n(t);for(var i=(new window.DOMParser).parseFromString(t,"text/html"),o=Object.keys(e),a=[].slice.call(i.body.querySelectorAll("*")),s=function(t,n){var...

             offset="0"/>
    </magic>
    <sub-class-of type="audio/ogg"/>
  </mime-type>

  <mime-type type="audio/opus">
    <_comment>Ogg Opus Codec Compressed WAV File</_comment>
    <magic priority="60">
      <!-- For a single stream file -->
      <match value="OggS\000.......................OpusHead" type="string"

			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}

	// Send heal request
	task := healTask{
		bucket:    source.bucket,
		object:    source.object,
		versionID: source.versionID,
		opts:      h.settings,
	}
	if source.opts != nil {
		task.opts = *source.opts
	} else {
		task.opts.ScanMode = madmin.HealNormalScan
	}

	h.countScanned(healType)

	if source.noWait {
		select {
		case globalBackgroundHealRoutine.tasks <- task:

### 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

TF_CAPI_EXPORT extern TFE_Context* TFE_NewContext(
    const TFE_ContextOptions* opts, TF_Status* status);
TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx);
TF_CAPI_EXPORT extern TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx,
                                                            TF_Status* status);

// Clears the internal caches in the TFE context. Useful when reseeding random
// ops.

    * Disabling TensorFloat-32 execution now causes TPUs to use float32 precision for float32 matmuls and other ops. TPUs have always used bfloat16 precision for certain ops, like matmul, when such ops had float32 inputs. Now, disabling TensorFloat-32 by calling `tf.config.experimental.enable_tensor_float_32_execution(False)` will cause TPUs to use float32 precision for such ops instead of bfloat16.

*  `tf.experimental.dtensor`

		}
	}
	uaAppend("; ", "")

	if cpus, err := gopsutilcpu.Info(); err == nil && len(cpus) > 0 {
		cpuMap := make(map[string]struct{}, len(cpus))
		coreMap := make(map[string]struct{}, len(cpus))
		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]

  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config(
      TF_CreateConfig(
          /*enable_xla_compilation=*/false,
          /*gpu_memory_allow_growth=*/true, /*num_cpu_devices=*/2),
      TF_DeleteBuffer);
  TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length,

  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(
      TFE_NewContextOptions(), TFE_DeleteContextOptions);
  std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config(
      TF_CreateConfig(
          /*xla*/ false,
          /* gpu_memory_allow_growth */ true, /* num_cpu_devices */
          2),
      TF_DeleteBuffer);
  TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length,