TF_Status* status);

// Returns the status for the tensor handle. In TFRT, a tensor handle can carry
// error info if error happens. If so, the status will be set with the error
// info. If not, status will be set as OK.
TF_CAPI_EXPORT extern void TFE_TensorHandleGetStatus(TFE_TensorHandle* h,
                                                     TF_Status* status);

    TF_Status* status);

// Get a list of input OpInfo::TensorProperties given node name.
// Return the serialized list `properties`.
TF_CAPI_EXPORT extern void TF_GetInputPropertiesList(
    TF_GraphProperties* graph_properties, const char* name,
    TF_Buffer** properties, int num_values, TF_Status* status);

// Get a list of output OpInfo::TensorProperties given node name.

  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, datatype, dims, num_dims, status);
  memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
  return th;
}

                                              TF_Status* status);

// Starts an in-process TensorFlow server.
TF_CAPI_EXPORT extern void TF_ServerStart(TF_Server* server, TF_Status* status);

// Stops an in-process TensorFlow server.
TF_CAPI_EXPORT extern void TF_ServerStop(TF_Server* server, TF_Status* status);

// Blocks until the server has been successfully stopped (via TF_ServerStop or

      new GradientTape<Gradient, BackwardFunction, TapeTensor>(false));
  AccumulatorCallState& call_state = call_state_.top();
  call_state.backward_tape = tape.get();
  auto pop_backward_tape =
      gtl::MakeCleanup([&call_state] { call_state.backward_tape = nullptr; });
  std::vector<Gradient*> forwardprop_aids;
  std::vector<int64_t> sources;
  std::unordered_set<int64_t> sources_set;

                                                 TF_Tensor* tensor,
                                                 TF_Status* status);
// Create a serialized tensorflow.ServerDef proto.
TF_Buffer* TFE_GetServerDef(const char* text_proto, TF_Status* status);

TF_CAPI_EXPORT extern void TF_MakeInternalErrorStatus(TF_Status* status,
                                                      const char* errMsg);

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_

#include "absl/status/statusor.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/platform/intrusive_ptr.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

                      int num_files, TF_Status** statuses);

  /// Obtains statistics for the given `path`.
  ///
  /// Updates `stats` only if `status` is set to `TF_OK`.
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if `path` exists.
  ///   * Must set `status` to `TF_NOT_FOUND` if `path` doesn't point to a
  ///     filesystem entry.
  ///   * Must set `status` to `TF_FAILED_PRECONDITION` if `path` is invalid.

  // When `reset_context` is true, initialize new cluster context state based
  // 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,

//   // forward `op`.
//   return new AddGradientFunction;
// }
//
// Status RegisterGradients(GradientRegistry* registry) {
//   return registry->Register("Add", AddRegisterer);
// }
class GradientFunction {
 public:
  virtual Status Compute(AbstractContext* ctx,
                         absl::Span<AbstractTensorHandle* const> grad_outputs,