if (it == cache_.end()) {
      return false;
    }
    lru_list_.erase(it->second.lru_iterator);
    if (timer_seconds_() - it->second.timestamp > max_age_) {
      cache_.erase(it);
      return false;
    }
    *value = it->second.value;
    lru_list_.push_front(it->first);
    it->second.lru_iterator = lru_list_.begin();
    return true;
  }

    TF_Status* status);

// Create and modify a variable placed on a parallel device which composes
// `first_device` and `second_device`.
void BasicTestsForTwoDevices(TFE_Context* context, const char* first_device,
                             const char* second_device);

// Implementations of templated functions ******************************

template <std::size_t num_replicas>

    if (grad_it == gradients.end()) {
      result[i] = nullptr;
    } else {
      if (grad_it->second.size() > 1) {
        Gradient* grad = vspace.AggregateGradients(grad_it->second);
        grad_it->second.clear();
        grad_it->second.push_back(grad);
      }
      result[i] = grad_it->second[0];
      used_gradient_ids.insert(tensor_id);
    }
  }
  VLOG(1) << "Final gradients size: "

// two component devices, running `x = TPUReplicatedInput(inputs=[a, b])` on the
// parallel device creates a parallel tensor `x` with `a` on the first of
// `underlying_devices` and `b` on the second. Running `a_unpacked, b_unpacked =
// TPUReplicatedOutput(input=x, num_replicas=2)` un-packs the parallel tensor
// into its components.
//
// The filled `device` struct and the allocated `device_info` struct may be

  // [Optional]
  // Create function for optimizer.
  void* (*create_func)();

  // Optimizer function for optimizer. The first param is an optimizer created
  // by create_func. The second param is input graph. The third param is
  // GrapplerItem. The fourth param is output graph.
  void (*optimize_func)(void*, const TF_Buffer*, const TF_GrapplerItem*,
                        TF_Buffer*, TF_Status*);

  // This is the size of cond_inputs, body_inputs, and body_outputs.
  const int ninputs;

  // The while condition graph. The inputs are the current values of the loop
  // variables. The output should be a scalar boolean.
  TF_Graph* const cond_graph;
  const TF_Output* const cond_inputs;
  TF_Output cond_output;

TF_CAPI_EXPORT extern uint64_t TF_NowNanos(void);

// Returns the number of microseconds since the Unix epoch.
TF_CAPI_EXPORT extern uint64_t TF_NowMicros(void);

// Returns the number of seconds since the Unix epoch.
TF_CAPI_EXPORT extern uint64_t TF_NowSeconds(void);

// Populates a TF_ThreadOptions struct with system-default values.
TF_CAPI_EXPORT extern void TF_DefaultThreadOptions(TF_ThreadOptions* options);

  absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord()
      override {
    return func_record_.GetNewRef();
  }

  // For LLVM style RTTI.
  static bool classof(const AbstractFunction* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  core::RefCountPtr<FunctionRecord> func_record_;
};
}  // namespace graph
}  // namespace tracing
}  // namespace tensorflow

TF_CAPI_EXPORT extern TFE_ContextOptions* TFE_NewContextOptions(void);

// Set the config in TF_ContextOptions.options.
// config should be a serialized tensorflow.ConfigProto proto.
// If config was not parsed successfully as a ConfigProto, record the
// error information in *status.
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig(
    TFE_ContextOptions* options, const void* proto, size_t proto_len,
    TF_Status* status);

                    BlockFetcher block_fetcher,
                    std::function<uint64_t()> timer_seconds = TF_NowSeconds)
      : block_size_(block_size),
        max_bytes_(max_bytes),
        max_staleness_(max_staleness),
        block_fetcher_(block_fetcher),
        timer_seconds_(timer_seconds),
        pruning_thread_(nullptr,
                        [](TF_Thread* thread) { TF_JoinThread(thread); }) {