*
   * <p>It is important to note that the primary purpose of the proxy object is to return control to
   * the caller when the timeout elapses; aborting the target method call is of secondary concern.
   * The particular nature and strength of the guarantees made by the proxy is
   * implementation-dependent. However, it is important that each of the methods on the target

  private val sinkBuffer: Buffer = sink.buffer
  private var writerClosed = false

  /** Lazily initialized on first use. */
  private var messageDeflater: MessageDeflater? = null

  // Masks are only a concern for client writers.
  private val maskKey: ByteArray? = if (isClient) ByteArray(4) else null
  private val maskCursor: Buffer.UnsafeCursor? = if (isClient) Buffer.UnsafeCursor() else null

          // otherwise try to presize a StringBuilder
          // it is kind of lame that we need to construct a decoder to access this value.
          // if this is a concern we could add special cases for some known charsets (like utf8)
          // or we could avoid inputstreamreader and use the decoder api directly

`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Since any tenants or users providing models, graphs or checkpoints can execute

  private val controlFrameBuffer = Buffer()
  private val messageFrameBuffer = Buffer()

  /** Lazily initialized on first use. */
  private var messageInflater: MessageInflater? = null

  // Masks are only a concern for server writers.
  private val maskKey: ByteArray? = if (isClient) null else ByteArray(4)
  private val maskCursor: Buffer.UnsafeCursor? = if (isClient) null else Buffer.UnsafeCursor()

  interface FrameCallback {

```

...path-nya adalah:

```
/items/foo
```

/// info

"path" juga biasa disebut "endpoint" atau "route".

///

ketika membuat API, "path" adalah jalan utama untuk memisahkan "concern" dan "resources".

#### Operasi

"Operasi" di sini merujuk ke salah satu dari metode HTTP berikut.

Salah satu dari:

* `POST`
* `GET`
* `PUT`
* `DELETE`

    status->status = tensorflow::errors::NotFound(
        op_name, " does not have number_attr() defined.");
    return nullptr;
  }

  // The returned string is owned by OpRegistry, so liveness is not a concern.
  return input_arg.number_attr().c_str();
}

int TF_OpIsStateful(const char* op_type, TF_Status* status) {
  const tensorflow::OpRegistrationData* op_reg_data;
  status->status =

			return err
		}, index)
	}
	return sys.collectPeerMetrics(ctx, peerChannels, g)
}

// GetSysConfig - Get information about system config
// (only the config that are of concern to minio)
func (sys *NotificationSys) GetSysConfig(ctx context.Context) []madmin.SysConfig {
	reply := make([]madmin.SysConfig, len(sys.peerClients))

	g := errgroup.WithNErrs(len(sys.peerClients))

// (only the config that are of concern to minio)
func (s *peerRESTServer) GetSysConfigHandler(_ *grid.MSS) (*grid.JSON[madmin.SysConfig], *grid.RemoteErr) {
	info := madmin.GetSysConfig(context.Background(), globalLocalNodeName)
	return madminSysConfig.NewJSONWith(&info), nil
}

// GetSysServicesHandler - returns system services information.
// (only the services that are of concern to minio)

	pr, pw := io.Pipe()
	// Copy input to compressor
	opts := compressOpts
	if encrypted {
		// The values used for padding are not a security concern,
		// but we choose pseudo-random numbers instead of just zeros.
		rng := rand.New(rand.NewSource(time.Now().UnixNano()))