// TODO(srbs): Add APIs for specifying attrs etc.
// `op_type` must outlive `op`.
void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s);
// `op_name` must outlive `op`.
void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,
                            TF_Status* s);
// `attr_name` must outlive `op`.

    TF_Status* status);

// Get a set of node names that must be preserved. They can not be transformed
// or removed during the graph transformation. This includes feed and fetch
// nodes, keep_ops, init_ops. Fills in `values` and `lengths`, each of which
// must point to an array of length at least `num_values`.
//
// The elements of values will point to addresses in `storage` which must be at

// There are currently no graph semantics implemented for registered custom
// devices, so executing tf.functions which contain operations placed on the
// custom devices will fail.
//
// `device_name` must not name an existing physical or custom device. It must
// follow the format:
//
//    /job:<name>/replica:<replica>/task:<task>/device:<type>:<device_num>
//
// If the device is successfully registered, `status` is set to TF_OK. Otherwise

// keep.

// `value` must point to a string of length `length` bytes.
TF_CAPI_EXPORT extern void TF_SetAttrString(TF_OperationDescription* desc,
                                            const char* attr_name,
                                            const void* value, size_t length);
// `values` and `lengths` each must have lengths `num_values`.
// `values[i]` must point to a string of length `lengths[i]` bytes.

// per device in `underlying_devices`. Implicit copies off of the device throw
// an error.
//
// All component tensors must have the same dtype. Currently they must also have
// the same shape, although this requirement may be relaxed in the future.
//
// `device_name` must not name an existing physical or custom device (see
// the documentation for TFE_RegisterCustomDevice for more information).
//

  // before `StartExecute` is called again. Using `StartExecute` with `Join`
  // allows the caller to schedule computation on multiple ParallelDevices
  // without sequencing those operations (first call `StartExecute` on each
  // parallel device, then call `Join` on each; even if some of the `Join`s
  // return a bad status the caller must run all of the `Join`s or any future

  //
  // WARNING: The destructor of this class is marked as protected to disallow
  // clients from directly destroying this object since it may manage its own
  // lifetime through ref counting. Thus clients MUST call Release() in order to
  // destroy an instance of this class.
  virtual void Release() = 0;

  // Creates an operation builder and ties it to this context.

                          const char* device, TF_Status* status);
  // Dereferences the backing buffer for the variable. Note that since this can
  // fail (it runs operations), it must be called explicitly and the resulting
  // `status` checked.
  void Destroy(TFE_Context* context, TF_Status* status);

  // Reads from the variable.
  TensorHandlePtr Read(TFE_Context* context, TF_Status* status);

TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true
// and sets 'device_name' accordingly.
// `device_type` must be either "GPU" or "TPU".
bool GetDeviceName(TFE_Context* ctx, tensorflow::string* device_name,
                   const char* device_type);

// Create a ServerDef with the given `job_name` and add `num_tasks` tasks in it.

//
// The given param (which may be null) is passed to work_func when the thread
// starts. In this way, data may be passed from the thread back to the caller.
//
// Caller takes ownership of the result and must call TF_JoinThread on it
// eventually.
TF_CAPI_EXPORT extern TF_Thread* TF_StartThread(const TF_ThreadOptions* options,
                                                const char* thread_name,