// 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,
                                      int64_t init_timeout_in_ms, int retries,

// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:
  explicit GraphFunction(FunctionDef fdef);
  ~GraphFunction() override;

  // GraphFunction maybe stay alive for the duration of the returned
  // FunctionDef.
  Status GetFunctionDef(const FunctionDef** fdef) override;

  // Returns a shared reference to the wrapped function.

TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status);

// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status,

// ServerDef must be up when the context is created.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx,
                                                   int keep_alive_secs,
                                                   const void* proto,
                                                   size_t proto_len,
                                                   TF_Status* status);

    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

      "TFE_ContextSetServerDef not supported on mobile");
  return false;
#else   // !defined(IS_MOBILE_PLATFORM)
  bool is_alive;
  status->status =
      tensorflow::unwrap(ctx)->GetDistributedManager()->CheckRemoteAlive(
          worker_name, &is_alive);
  return is_alive;
#endif  // !IS_MOBILE_PLATFORM
}

TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,

    return;
  }
  status->status = coord_agent->WaitAtBarrier(
      barrier_id, absl::Milliseconds(barrier_timeout_in_ms), {});
}

void TFE_InitializeLocalOnlyContext(TFE_Context* ctx, int keep_alive_secs,
                                    const void* proto, size_t proto_len,
                                    TF_Status* status) {
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {

*   Other:
    *   Add necessary shape util support for bfloat16.
    *   Add a way to run ops using a step function to MonitoredSession.
    *   Add `DenseFlipout` probabilistic layer.
    *   A new flag `ignore_live_threads` is available on train. If set to
        `True`, it will ignore threads that remain running when tearing down
        infrastructure after successfully completing training, instead of
        throwing a RuntimeError.

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class GradientTape {
 public:
  // If `persistent` is true, GradientTape will not eagerly delete backward
  // functions (and hence the tensors they keep alive). Instead, everything
  // is deleted in ~GradientTape. Persistent GradientTapes are useful when
  // users want to compute multiple gradients over the same tape.

  // `StartExecute`s will deadlock).
  //
  // If `is_async=false` (constructor argument), `cancellation_manager` must
  // live until `Join` finishes. If `is_async=true` it must live until `Join` is
  // followed by `TFE_ContextAsyncWait` to clear pending operations. It will be
  // used to cancel all other operations if any fails.
  //