// 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.

#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())) {

// 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

  // 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;

// removal of the registration part from `Env` and adding it here instead: we
// will need tables to hold for each scheme the function tables that implement
// the needed functionality instead of the current `FileSystemRegistry` code in
// `core/platform/env.cc`.
class ModularFileSystem final : public FileSystem {
 public:
  ModularFileSystem(
      std::unique_ptr<TF_Filesystem> filesystem,

      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,
                          const char* node_name)