// you want the latter, use TFE_TensorHandleBackingDeviceName. This function
// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

// Returns the name of the device in whose memory `h` resides.
//
// This function will block till the operation that produces `h` has completed.

  virtual const string& Name() const = 0;

  // Returns the operation's device name.
  //
  // The value returned may be different from the one set by SetDeviceName, but
  // it will be compatible with it: the name will be updated by device placement
  // logic to refer to the specific device chosen.
  //
  // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value

// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });
//     // No matter what, fclose(fp) will happen.
//     DataObject d;
//     while (ReadDataObject(fp, &d)) {

  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.
  virtual Status SetOrUpdateServerDef(const ServerDef& server_def,
                                      bool reset_context, int keep_alive_secs,

#include "tensorflow/c/kernels.h"

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's

      TF_GUARDED_BY(mu);
  bool delete_requested TF_GUARDED_BY(mu);  // set true by TF_DeleteGraph

  // Used to link graphs contained in TF_WhileParams to the parent graph that
  // will eventually contain the full while loop.
  TF_Graph* parent;
  TF_Output* parent_inputs;
};

struct TF_OperationDescription {
  TF_OperationDescription(TF_Graph* g, const char* op_type,

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

  std::string DebugString() const override;

  // Returns a Boolean hint indicating whether callers should prefer

                           TF_Status*);

// TF_ExecuteOperation will, if in eager mode, execute, if in graph mode, maybe
// capture some inputs and then add a node in the graph. The output tensors are
// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.
void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,

                                                TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.
// for a GPU tensor this will return a pointer to GPU memory). The pointer is

 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */
Status CalcNumericalGrad(AbstractContext* ctx, Model forward,