// Returns the device placement policy to be used by this context in the current
// thread.
TF_CAPI_EXPORT extern TFE_ContextDevicePlacementPolicy
TFE_ContextGetDevicePlacementPolicy(TFE_Context* ctx);

// A tensorflow.ServerDef specifies remote workers (in addition to the current
// workers name). Operations created in this context can then be executed on

  // Check if the remote task is alive.
  virtual Status CheckRemoteAlive(const std::string& remote_task_name,
                                  bool* is_alive) = 0;

  // Get pointer to the coordination service agent instance.
  virtual tsl::CoordinationServiceAgent* GetCoordinationServiceAgent() = 0;
};
}  // namespace tensorflow

// Sets a custom Executor for the current thread. All nodes created by this
// thread will be added to this Executor. It will override the current executor.
TF_CAPI_EXPORT extern void TFE_ContextSetExecutorForThread(TFE_Context*,
                                                           TFE_Executor*);

// Returns the Executor for the current thread.

void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s);

// Creates a new TF_AbstractFunction from the current tracing states in the
// context. The provided `ctx` is consumed by this API call and deleted.
// The returned TF_AbstractFunction must be deleted by the client,

#include "tensorflow/c/eager/c_api.h"

#ifdef __cplusplus
extern "C" {
#endif

// Test only exports of the monitoring Cell Reader API which allows tests to
// read current values from streamz counters defined in other modules.
//
// The code under test will have created streamz counters like this:
// auto* streamz = tensorflow::monitoring::Counter<1>::New("name",
// "description", "label");

                                                 int max_inputs);

// Get the number of current consumers of a specific output of an
// operation.  Note that this number can change when new operations
// are added to the graph.
TF_CAPI_EXPORT extern int TF_OperationOutputNumConsumers(TF_Output oper_out);

// Get list of all current consumers of a specific output of an
// operation.  `consumers` must point to an array of length at least

  in_grads.reserve(input_tensors.size());
  for (int target_index = 0; target_index < input_tensors.size();
       ++target_index) {
    const auto current_grad =
        accumulated_gradients_.find(input_tensors[target_index].GetID());
    if (current_grad == accumulated_gradients_.end()) {
      if (IsDtypeTrainable(input_tensors[target_index].GetDType())) {

  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager
  // Executor is only used in current TF but not in TFRT.
  virtual EagerExecutor& Executor() = 0;

    TF_CoordinationServiceAgent* agent);

TF_CAPI_EXPORT extern void TF_CoordinationServiceInsertKeyValue(
    const char* key, int64_t key_size, const char* value, int64_t value_size,
    TF_CoordinationServiceAgent* agent, TF_Status* status);

// Obtains key-value from coordination service agent. The returned `TF_Buffer`
// is a newly allocated buffer to hold the string key-value, and caller is

  /// Accessors for cache parameters.
  size_t block_size() const { return block_size_; }
  size_t max_bytes() const { return max_bytes_; }
  uint64_t max_staleness() const { return max_staleness_; }

  /// The current size (in bytes) of the cache.
  size_t CacheSize() const ABSL_LOCKS_EXCLUDED(mu_);

  // Returns true if the cache is enabled. If false, the BlockFetcher callback
  // is always executed during Read.