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typedef enum TF_TriState {
  TF_TriState_Default = 0,
  TF_TriState_Off,
  TF_TriState_On,
} TF_TriState;

// TF_GrapplerItem represents a combination of a graph, one of more fetch nodes,
// and potentially a set of nodes to feed.
typedef struct TF_GrapplerItem TF_GrapplerItem;

// Flags indicating whether existing optimizers should be turned off.
// It's optional for plugin to set functions to return true/false. If not

  TF_Operation* op;
  Split3Helper(input, graph, s, name, &op);
  return op;
}

bool IsPlaceholder(const tensorflow::NodeDef& node_def) {
  if (node_def.op() != "Placeholder" || node_def.name() != "feed") {
    return false;
  }
  bool found_dtype = false;
  bool found_shape = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {

    // If this file does not exist on server, we will need to sync it.
    bool sync_need = (status_code == TF_NOT_FOUND);
    file->plugin_file = new tf_writable_file::GCSFile(
        {std::move(bucket), std::move(object), &gcs_file->gcs_client,
         TempFile(temp_file_name, std::ios::binary | std::ios::app), sync_need,
         -1});
  } else {
    // If compose is true, we do not download anything.

                                                           TF_DeleteStatus);

  TF_Operation* feed = Placeholder(func_graph.get(), s.get());
  TF_Operation* neg = Neg(feed, func_graph.get(), s.get());

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,

  // CustomDeviceOpHandler.
  virtual Status RegisterCustomDevice(const string& name,
                                      std::unique_ptr<CustomDevice> device) = 0;

  // Return FunctionLibraryDefinition. Transformations need to use it to use it
  // to invoke MLIR compiler passes.
  virtual FunctionLibraryDefinition* FuncLibDef() = 0;

  // Resets the global rendezvous used for functions.
  virtual void ResetGlobalRendezvousForFunction() = 0;

  TF_RETURN_IF_ERROR(ValidatePluginMemoryRoutines(info));

  // Validate and register all filesystems
  // Try to register as many filesystems as possible.
  // Free memory once we no longer need it
  Status status;
  for (int i = 0; i < info->num_schemes; i++) {
    status.Update(ValidateAndRegisterFilesystems(info, i));
    info->plugin_memory_free(info->ops[i].scheme);

 private:
  friend class GraphContext;  // For access to op_.
  TF_Graph* g_;
  std::unique_ptr<TF_OperationDescription> op_;
  // Hold `op_type` and `op_name` till both are available since we need both
  // to build a graph operation.
  string op_type_;
  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

    return nullptr;
  }
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  // TODO(apassos) do we need to wrap the deallocator here to make sure to sync
  // the device?
  TF_ManagedBuffer* buf =
      new TF_ManagedBuffer(data, len, deallocator, deallocator_arg,
                           /*owns_memory=*/false);

namespace tf_gcs_filesystem {

bool RamFileBlockCache::BlockNotStale(const std::shared_ptr<Block>& block) {
  absl::MutexLock l(&block->mu);
  if (block->state != FetchState::FINISHED) {
    return true;  // No need to check for staleness.
  }
  if (max_staleness_ == 0) return true;  // Not enforcing staleness.
  return timer_seconds_() - block->timestamp <= max_staleness_;
}