#ifdef SWIG
#define TF_CAPI_EXPORT
#else
#if defined(_WIN32)
#ifdef TF_COMPILE_LIBRARY
#define TF_CAPI_EXPORT __declspec(dllexport)
#else
#define TF_CAPI_EXPORT __declspec(dllimport)
#endif  // TF_COMPILE_LIBRARY
#else
#ifdef TF_CAPI_WEAK
#define TF_CAPI_EXPORT \
  __attribute__((visibility("default"))) __attribute((weak))
#else
#define TF_CAPI_EXPORT __attribute__((visibility("default")))

    return nullptr;
  }
  return new TFE_TensorDebugInfo(dev_dims);
}

TF_CAPI_EXPORT extern void TFE_DeleteTensorDebugInfo(
    TFE_TensorDebugInfo* debug_info) {
  delete debug_info;
}

TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info) {
  return debug_info->dev_dims.size();
}

TF_CAPI_EXPORT extern int64_t TFE_TensorDebugInfoOnDeviceDim(

                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
            std::unique_ptr<TensorSliceReader::VarToDataTypeMap> >

};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

  // The keys of this map are all the active sessions using this graph. Each

                                       remote_func_outputs, has_packed_input);
}

TEST(CAPI, RemoteExecuteSilentCopiesAsyncFunc) {
  TestRemoteExecuteSilentCopiesFunc(/*async=*/true, /*remote=*/true,
                                    /*heavy_load_on_streaming_rpc=*/false);
}
TEST(CAPI, RemoteExecuteSilentCopiesFuncRemoteOutputs) {
  TestRemoteExecuteSilentCopiesFunc(/*async=*/false, /*remote=*/true,

                       int64_t delta = 1);

TEST(CAPI, MonitoringCellReader0) {
  auto counter_name = "test/counter0";
  auto* counter = CreateCounter0(counter_name);
  auto* reader = TFE_MonitoringNewCounterReader(counter_name);
  IncrementCounter0(counter);

  int64_t actual = TFE_MonitoringReadCounter0(reader);

  CHECK_EQ(actual, 1);
}

TEST(CAPI, MonitoringCellReader1) {
  auto counter_name = "test/counter1";

}

TF_CAPI_EXPORT extern uint64_t TF_NowNanos(void) {
  return ::tensorflow::Env::Default()->NowNanos();
}

// Returns the number of microseconds since the Unix epoch.
TF_CAPI_EXPORT extern uint64_t TF_NowMicros(void) {
  return ::tensorflow::Env::Default()->NowMicros();
}

// Returns the number of seconds since the Unix epoch.
TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void) {

}

TEST(CAPI, RemoteExecuteSilentCopies) {
  TestRemoteExecuteSilentCopiesOp(/*async=*/false, /*remote=*/true);
}
TEST(CAPI, RemoteExecuteSilentCopiesAsync) {
  TestRemoteExecuteSilentCopiesOp(/*async=*/true, /*remote=*/true);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocal) {
  TestRemoteExecuteSilentCopiesOp(/*async=*/false, /*remote=*/false);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocalAsync) {

// 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,

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture...