//
// 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);

typedef struct TF_Buffer {
  const void* data;
  size_t length;
  void (*data_deallocator)(void* data, size_t length);
} TF_Buffer;

// Makes a copy of the input and sets an appropriate deallocator.  Useful for
// passing in read-only, input protobufs.
TF_CAPI_EXPORT extern TF_Buffer* TF_NewBufferFromString(const void* proto,
                                                        size_t proto_len);

    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);

  auto dtype = TFE_TensorHandleDataType(t->tensor);
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &LoggedTensorNumDims;
  handle_methods.dim = &LoggedTensorDim;
  handle_methods.deallocator = &LoggedTensorDeallocator;
  return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(),
                                         dtype, t.release(), handle_methods,

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

  ParallelTensor* t_released = t.release();
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &ParallelTensorNumDims;
  handle_methods.dim = &ParallelTensorDim;
  handle_methods.deallocator = &ParallelTensorDeallocator;
  handle_methods.summarize = &ParallelTensorSummarize;
  return TensorHandlePtr(TFE_NewCustomDeviceTensorHandle(
      context, parallel_device_name.c_str(), t_released->dtype(), t_released,

	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()