if (!status->status.ok()) {
    delete lib_handle;
    return nullptr;
  }
  return lib_handle;
}

TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; }

void TF_DeleteLibraryHandle(TF_Library* lib_handle) {
  if (lib_handle == nullptr) return;
  tensorflow::port::Free(const_cast<void*>(lib_handle->op_list.data));
  delete lib_handle;
}

TF_Buffer* TF_GetAllOpList() {

	// There is flow control in both directions.
	StreamHandler struct {
		// Handle an incoming request. Initial payload is sent.
		// Additional input packets (if any) are streamed to request.
		// Upstream will block when request channel is full.
		// Response packets can be sent at any time.
		// Any non-nil error sent as response means no more responses are sent.
		Handle StreamHandlerFn

		// Subroute for handler.

* **Cloud services** that handle this for you
    * The cloud service will probably **handle replication for you**. It would possibly let you define **a process to run**, or a **container image** to use, in any case, it would most probably be **a single Uvicorn process**, and the cloud service would be in charge of replicating it.

!!! tip

#### Docker Compose

You could be deploying to a **single server** (not a cluster) with **Docker Compose**, so you wouldn't have an easy way to manage replication of containers (with Docker Compose) while preserving the shared network and **load balancing**.

Up to this point, with all the tutorials in the docs, you have probably been running a **server program** like Uvicorn, running a **single process**.

When deploying applications you will probably want to have some **replication of processes** to take advantage of **multiple cores** and to be able to handle more requests.

		EnvVar: "MINIO_SHUTDOWN_TIMEOUT",
		Hidden: true,
	},
	cli.DurationFlag{
		Name:   "idle-timeout",
		Value:  xhttp.DefaultIdleTimeout,
		Usage:  "idle timeout is the maximum amount of time to wait for the next request when keep-alive are enabled",
		EnvVar: "MINIO_IDLE_TIMEOUT",
		Hidden: true,
	},
	cli.DurationFlag{
		Name:   "read-header-timeout",
		Value:  xhttp.DefaultReadHeaderTimeout,

	}
	if err != nil {
		return err
	}
	// Signal that we are reconnected, update state and handle messages.
	// Prevent other connections from connecting while we process.
	c.reconnectMu.Lock()
	if c.remoteID != nil {
		c.reconnected()
	}
	rid := uuid.UUID(req.ID)
	c.remoteID = &rid

	// Handle incoming messages until disconnect.
	c.handleMessages(ctx, conn)
	return nil
}

    if (it != loaded_libs->end()) {
      lib_handle->lib_handle = it->second;
    } else {
      status->status =
          env->LoadDynamicLibrary(library_filename, &lib_handle->lib_handle);
      if (status->status.ok()) {
        TF_CHECK_OK(
            tensorflow::RegisterPluggableDevicePlugin(lib_handle->lib_handle));
      } else {
        delete lib_handle;
        return nullptr;
      }
    }

	IdleSpeed    = "idle_speed"
	EnvIdleSpeed = "MINIO_SCANNER_IDLE_SPEED"

	ExcessVersions    = "alert_excess_versions"
	EnvExcessVersions = "MINIO_SCANNER_ALERT_EXCESS_VERSIONS"

	ExcessFolders    = "alert_excess_folders"
	EnvExcessFolders = "MINIO_SCANNER_ALERT_EXCESS_FOLDERS"

	// All below are deprecated in October 2022 and
	// replaced them with a single speed parameter
	Delay            = "delay"

		return nil, fmt.Errorf("grid: local host not found")
	}

	return m, nil
}

// AddToMux will add the grid manager to the given mux.
func (m *Manager) AddToMux(router *mux.Router) {
	router.Handle(RoutePath, m.Handler())
}

// Handler returns a handler that can be used to serve grid requests.
// This should be connected on RoutePath to the main server.
func (m *Manager) Handler() http.HandlerFunc {