// this node returns a 404
func proxyHeadToReplicationTarget(ctx context.Context, bucket, object string, rs *HTTPRangeSpec, opts ObjectOptions, proxyTargets *madmin.BucketTargets) (oi ObjectInfo, proxy proxyResult) {
	_, oi, proxy = proxyHeadToRepTarget(ctx, bucket, object, rs, opts, proxyTargets)
	return oi, proxy
}

func scheduleReplication(ctx context.Context, oi ObjectInfo, o ObjectLayer, dsc ReplicateDecision, opType replication.Type) {

	memAvailableMD = NewGaugeMD(memAvailable, "Available memory on the node")
)

// loadMemoryMetrics - `MetricsLoaderFn` for node memory metrics.
func loadMemoryMetrics(ctx context.Context, m MetricValues, c *metricsCache) error {
	memMetrics, err := c.memoryMetrics.Get()
	if err != nil {
		metricsLogIf(ctx, err)
		return err
	}

	m.Set(memTotal, float64(memMetrics.Total))

		"Total number of messages sent since start",
		targetID)
)

// loadAuditMetrics - `MetricsLoaderFn` for audit
// such as failed messages and total messages.
func loadAuditMetrics(_ context.Context, m MetricValues, c *metricsCache) error {
	audit := logger.CurrentStats()
	for id, st := range audit {
		labels := []string{targetID, id}
		m.Set(auditFailedMessages, float64(st.FailedMessages), labels...)

		return store.ErrNotConnected
	}

	return nil
}

// NewWebhookTarget - creates new Webhook target.
func NewWebhookTarget(ctx context.Context, id string, args WebhookArgs, loggerOnce logger.LogOnce, transport *http.Transport) (*WebhookTarget, error) {
	ctx, cancel := context.WithCancel(ctx)

	var queueStore store.Store[event.Event]
	if args.QueueDir != "" {

				if err != nil {
					return err
				}
				pod, err := kubeClient.Kube().CoreV1().Pods(podNs).Get(context.TODO(), podName, metav1.GetOptions{})
				if err != nil {
					return err
				}
				ns, err := kubeClient.Kube().CoreV1().Namespaces().Get(context.TODO(), podNs, metav1.GetOptions{})
				if err != nil {
					return err
				}
				podLabels = pod.GetLabels()
				nsLabels = ns.GetLabels()

	}
}

func initConfigSubsystem(ctx context.Context, newObject ObjectLayer) error {
	// %w is used by all error returns here to make sure
	// we wrap the underlying error, make sure when you
	// are modifying this code that you do so, if and when
	// you want to add extra context to your error. This
	// ensures top level retry works accordingly.

	// Initialize config system.

  }
};

// An abstract operation describes an operation by its type, name, and
// attributes. It can be "executed" by the context with some input tensors.
// It is allowed to reusing the same abstract operation for multiple execution
// on a given context, with the same or different input tensors.
class TracingOperation : public AbstractOperation {
 protected:
  explicit TracingOperation(AbstractOperationKind kind)

};

absl::optional<std::vector<MaybeParallelTensorOwned>> ExecuteWithSpecialOps(
    const ParallelDevice& parallel_device,
    const std::string& parallel_device_name, TFE_Context* context,
    std::vector<MaybeParallelTensorUnowned> inputs, const char* operation_name,
    const TFE_OpAttrs* attributes, int expected_max_outputs,
    TF_Status* status) {

    protected int doExecute(DefaultEncryptInvoker.LocalContext context) throws Exception {
        String encrypted = context.reader.readLine("Enter the password to decrypt: ");
        if (secDispatcher.isAnyEncryptedString(encrypted)) {
            context.terminal.writer().println(secDispatcher.decrypt(encrypted));
            return OK;
        } else {

// Creates a new TensorFlow function. A Function is an execution context, and as
// such it can trace operations through TF_ExecuteOperation. After completing
// tracing, a function can be obtained by TF_FinalizeFunction.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* status);

// Creates a context for eager execution of operations.
TF_ExecutionContext* TF_NewEagerExecutionContext(TFE_ContextOptions*,