Key:         config.APISubSys,
			Description: "manage global HTTP API call specific features, such as throttling, authentication types, etc.",
		},
		config.HelpKV{
			Key:         config.ScannerSubSys,
			Description: "manage namespace scanning for usage calculation, lifecycle, healing and more",
		},
		config.HelpKV{
			Key:         config.BatchSubSys,
			Description: "manage batch job workers and wait times",
		},

//
// auto* reader = TFE_MonitoringNewCounterReader("name");
// test();
// int64_t value = TFE_MonitoringReadCounter1(reader, "label-value");

// Opaque handle to a reader.
typedef struct TFE_MonitoringCounterReader TFE_MonitoringCounterReader;

// Returns a handle to be used for reading values from streamz counter. The
// counter can have been created with any number of labels.
TF_CAPI_EXPORT extern TFE_MonitoringCounterReader*

	remoteHost := remote.HostName()

	// 1: Echo
	register := func(manager *Manager) {
		errFatal(manager.RegisterStreamingHandler(handlerTest, StreamHandler{
			Handle: func(ctx context.Context, payload []byte, request <-chan []byte, resp chan<- []byte) *RemoteErr {
				for in := range request {
					b := append([]byte{}, payload...)
					b = append(b, in...)
					resp <- b
				}
				t.Log(GetCaller(ctx).Name, "Handler done")

// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least
// one of the source or destination devices to be CPU (i.e., for the source or
// destination tensor to be placed in host memory).
// If async execution is enabled, the copy may be enqueued and the call will
// return "non-ready" handle. Else, this function returns after the copy has
// been done.

        "The data was too large to fit into the specified buffer.", "A device attached to the system is not functioning.", "Incorrect function.",
        "The parameter is incorrect.", "Invalid access to memory location.", "The handle is invalid.", "The parameter is incorrect.",
        "The system cannot find the file specified.", "The system cannot find the file specified.", "End of file", "More data is available.",

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

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import jcifs.smb1.dcerpc.*;

public class MsrpcSamrOpenDomain extends samr.SamrOpenDomain {

    public MsrpcSamrOpenDomain(SamrPolicyHandle handle,
                    int access,
                    rpc.sid_t sid,
                    SamrDomainHandle domainHandle) {
        super(handle, access, sid, domainHandle);
        ptype = 0;
        flags = DCERPC_FIRST_FRAG | DCERPC_LAST_FRAG;
    }

const char* const kDlTensorCapsuleName = "dltensor";

// Converts eager tensor handle to DLPack (DLManagedTensor*), and return the
// void* for further PyCapsule construction.
TF_CAPI_EXPORT extern void* TFE_HandleToDLPack(TFE_TensorHandle* h,
                                               TF_Status* status);

// Converts DLPack (DLManagedTensor*) to eager tensor handle.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_HandleFromDLPack(void* dlm,

  }
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  tensorflow::TensorHandle* handle = nullptr;
  status->status = tensorflow::TensorHandle::CreatePackedHandle(
      std::move(tensor_handles), context, &handle);
  return tensorflow::wrap(handle);
}

void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx, unsigned char enable,