}
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}

static TracingContext* CreateTracingExecutionContext(const char* fn_name,
                                                     TF_Status* s) {
  if (default_factory) {
    return default_factory(fn_name, s);
  }
  tsl::Set_TF_Status_from_Status(
      s, errors::FailedPrecondition("default_factory is nullptr"));
  return nullptr;
}

};

struct TFE_MonitoringBuckets {
  explicit TFE_MonitoringBuckets(
      std::function<std::unique_ptr<tensorflow::monitoring::Buckets>(void)>
          fn) {
    create_buckets = fn;
  }

  std::function<std::unique_ptr<tensorflow::monitoring::Buckets>(void)>
      create_buckets;
};

struct TFE_MonitoringSamplerCell {
  tensorflow::monitoring::SamplerCell cell;
};

// Performs various checks.
Status ComputeBodyNodes(
    const TF_Graph* fn_body, const char* fn_name, int num_opers,
    const TF_Operation* const* opers,
    const std::unordered_map<const Node*, std::vector<int>>& input_nodes,
    std::vector<const Node*>* body_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  if (num_opers == -1) {
    for (const Node* node : fn_body->graph.op_nodes()) {
      const auto& iter = input_nodes.find(node);

  TF_Output* parent_inputs = params->cond_graph->parent_inputs;
  int num_loop_vars = params->ninputs;

  mutex_lock l(parent->mu);

  // 'cond_fn' copies the cond graph into the parent graph.
  tensorflow::ops::CondGraphBuilderFn cond_fn =
      [params, parent](const tensorflow::Scope& scope,
                       const std::vector<tensorflow::Output>& inputs,
                       tensorflow::Output* output) {

// Create a TF_Function from a TF_Graph
//
// Params:
//  fn_body - the graph whose operations (or subset of whose operations) will be
//            converted to TF_Function.
//  fn_name - the name of the new TF_Function. Should match the operation
//            name (OpDef.name) regexp [A-Z][A-Za-z0-9_.\\-/]*.
//            If `append_hash_to_fn_name` is false, `fn_name` must be distinct

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build the abstract op to run the function.
  TF_AbstractOp* fn_op = TF_NewAbstractOp(eager_execution_ctx);
  TF_AbstractOpSetOpType(fn_op, fn_name.c_str(), status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build an abstract input tensor.
  TFE_Context* eager_ctx =

  TF_Output y{reduce_op, 0};
  const char* output_name = "y";
  std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)> function(
      TF_GraphToFunction(
          /* fn_body */ body.get(), /* fn_name */ function_name,
          /* append_hash_to_fn_name */ 0, /* num_opers */ 2,
          /* opers */ operations, /* ninputs */ 1, /* inputs */ &x,
          /* noutputs */ 1, /* outputs */ &y, /* output_names */ &output_name,

  forward_op_.attrs.BuildNodeDef();
  // TODO(b/170307493): Populate skip_input_indices here.
  std::unique_ptr<GradientFunction> backward_fn;
  TF_RETURN_IF_ERROR(registry_.Lookup(forward_op_, &backward_fn));
  tape_->RecordOperation(forward_op_.inputs, forward_op_.outputs,
                         backward_fn.release(), parent_op_->Name());
  return OkStatus();
}

}  // namespace gradients

// Registers Graph optimizers.
Status InitGraphPlugin(void* dso_handle);

// Allow registering a graph optimizer using a function (used for
// testing).
Status InitGraphPlugin(TFInitGraphPluginFn init_fn);

struct GrapplerItem;
class Cluster;

struct TFStatusDeleter {
  void operator()(TF_Status* s) const { TF_DeleteStatus(s); }
};
using OwnedTFStatus = std::unique_ptr<TF_Status, TFStatusDeleter>;

#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Builds and returns a `TracingContext` using the default tracing impl.
AbstractContext* BuildFunction(const char* fn_name);

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,