//
// The data will be deallocated by a subsequent call to TF_DeleteTensor via:
//      (*deallocator)(data, len, deallocator_arg)
// Clients must provide a custom deallocator function so they can pass in
// memory managed by something like numpy.
//
// May return NULL (and invoke the deallocator) if the provided data buffer
// (data, len) is inconsistent with a tensor of the given TF_DataType

  }
};

// Allocates tensor data buffer using specified allocator.
// `operation` is a name for this operation.
void* allocate_tensor(const char* operation, size_t len, Allocator* allocator);

// Deallocates tensor data buffer.
// Defaults to deallocating using CPU allocator. You can pass pointer to
// a different Allocator as `arg`.
void deallocate_buffer(void* data, size_t len, void* arg);

  Allocator* allocator = nullptr;
  if (arg == nullptr) {
    allocator = cpu_allocator();
  } else {
    allocator = reinterpret_cast<Allocator*>(arg);
  }
  if (LogMemory::IsEnabled() && data != nullptr) {
    LogMemory::RecordRawDeallocation(
        "TensorFlow C Api", LogMemory::EXTERNAL_TENSOR_ALLOCATION_STEP_ID, data,
        allocator, false);
  }
  allocator->DeallocateRaw(data);

    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status) {
  tensorflow::Device* device = nullptr;
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  status->status = context->FindDeviceFromName(device_name, &device);
  if (!status->status.ok()) {
    deallocator(data, len, deallocator_arg);

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
    TFE_Context* ctx, const char* device_name, TF_DataType, const int64_t* dims,
    int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status);

// Retrieves the address space (i.e. job, replia, task) of the local host and
// saves it in the buffer.

  TF_TString_Init(&tstr[0]);
  TF_TString_Copy(&tstr[0], test_string, sizeof(test_string) - 1);

  auto deallocator = [](void* data, size_t len, void* arg) {};
  unique_tensor_ptr t_in(TF_NewTensor(TF_STRING, nullptr, 0, &tstr[0],
                                      sizeof(tstr), deallocator, nullptr),
                         TF_DeleteTensor);

  // Create a TF_Operation with the attribute t_in

	return len(ips)
}

// for testing, it assumes this is the allocator is unique for the ipFamily
func (a *Allocator) Free() int {
	return int(a.size) - a.Used()
}

// Destroy
func (a *Allocator) Destroy() {
}

// DryRun
func (a *Allocator) DryRun() Interface {
	return dryRunAllocator{a}
}

// EnableMetrics
func (a *Allocator) EnableMetrics() {
	registerMetrics()

	}

	return a, err
}

// NewInMemory creates an in-memory allocator.
func NewInMemory(pr net.PortRange) (*PortAllocator, error) {
	return New(pr, func(max int, rangeSpec string, offset int) (allocator.Interface, error) {
		return allocator.NewAllocationMapWithOffset(max, rangeSpec, offset), nil
	})
}

// NewFromSnapshot allocates a PortAllocator and initializes it from a snapshot.

		allocator, err := r.getAllocator(netutils.ParseIPSloppy("10.0.0.11"))
		if err != nil {
			return false, nil
		}
		allocator.ipAddressSynced = func() bool { return true }
		return allocator.ready.Load(), nil
	})
	if err != nil {
		t.Fatal(err)
	}
	// allocate one IP from the new allocator
	err = r.Allocate(netutils.ParseIPSloppy("10.0.0.11"))

func (c *MetaAllocator) AllocateService(service *api.Service, ip net.IP) error {
	allocator, err := c.getAllocator(ip)
	if err != nil {
		return err
	}
	return allocator.AllocateService(service, ip)
}

func (c *MetaAllocator) Allocate(ip net.IP) error {
	allocator, err := c.getAllocator(ip)
	if err != nil {
		return err
	}
	return allocator.Allocate(ip)
}