DeviceNameUtils::ParsedName parsed_name;
  tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name);
  std::string device_type = parsed_name.type;
  int device_id = 0;
  if (parsed_name.has_id) {
    device_id = parsed_name.id;
  }

  ctx.device_id = device_id;
  if (device_type == "CPU") {
    ctx.device_type = DLDeviceType::kDLCPU;
  } else if (device_type == "GPU") {
#if TENSORFLOW_USE_ROCM

	"os/exec"
	"path/filepath"
	"runtime"
	"strings"
	"syscall"
)

const debug = false

var tmpdir string

var (
	devID    string
	appID    string
	teamID   string
	bundleID string
	deviceID string
)

// lock is a file lock to serialize iOS runs. It is global to avoid the
// garbage collector finalizing it, closing the file and releasing the
// lock prematurely.
var lock *os.File

func main() {

  // Returns the device type which created the handle.
  virtual const char* DeviceType(absl::Status* status) const = 0;
  // Returns the device ID which created the handle.
  virtual int DeviceId(absl::Status* status) const = 0;
  // Returns a tensor for the handle. If tensor is remote, it will be copied.
  virtual AbstractTensorInterface* Resolve(absl::Status* status) = 0;

  std::string DebugString() const override;

      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.
  std::unique_ptr<ParallelTensor> DeviceIDs(TFE_Context* context,
                                            TF_Status* status) const;

  // The number of devices operations run on.
  size_t num_underlying_devices() const { return underlying_devices_.size(); }

    components.emplace_back(t);
  }
  return ParallelTensor::FromTensorHandles(*this, std::move(components),
                                           status);
}

std::unique_ptr<ParallelTensor> ParallelDevice::DeviceIDs(
    TFE_Context* context, TF_Status* status) const {
  std::vector<int32_t> ids;
  ids.reserve(num_underlying_devices());
  for (int i = 0; i < num_underlying_devices(); ++i) {
    ids.push_back(i);
  }

int TFE_TensorHandleDeviceID(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }
  return tensorflow::unwrap(h)->DeviceId(&status->status);
}

TF_CAPI_EXPORT extern void TFE_TensorHandleGetStatus(TFE_TensorHandle* h,
                                                     TF_Status* status) {