// API for driving Graph execution.

typedef struct TF_Session TF_Session;

// Return a new execution session with the associated graph, or NULL on
// error. Does not take ownership of any input parameters.
//
// *`graph` must be a valid graph (not deleted or nullptr). `graph` will be
// kept alive for the lifetime of the returned TF_Session. New nodes can still
// be added to `graph` after this call.

  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create a simple graph.
  Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
  TF_Operation* oper = ScalarConst(3, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
  Neg(oper, graph, s);

    }
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.
  void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
           std::initializer_list<TF_Output> outputs,
           const std::vector<int32_t>& expected_results) {
    // Create a session for this graph.
    CSession csession(host_graph_, s_);

 *
 * <p>Usage example:
 *
 * <pre>{@code
 * LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
 *     .maximumSize(10000)
 *     .expireAfterWrite(Duration.ofMinutes(10))
 *     .removalListener(MY_LISTENER)
 *     .build(
 *         new CacheLoader<Key, Graph>() {
 *           public Graph load(Key key) throws AnyException {
 *             return createExpensiveGraph(key);
 *           }

 *
 * <p>Usage example:
 *
 * <pre>{@code
 * LoadingCache<Key, Graph> graphs = CacheBuilder.newBuilder()
 *     .maximumSize(10000)
 *     .expireAfterWrite(Duration.ofMinutes(10))
 *     .removalListener(MY_LISTENER)
 *     .build(
 *         new CacheLoader<Key, Graph>() {
 *           public Graph load(Key key) throws AnyException {
 *             return createExpensiveGraph(key);
 *           }

  int num_retvals = 1;
  TFE_Op* op = TFE_NewOp(ctx, "AddFunction", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Add a config_proto attr, to trigger grappler graph rewrites in the current
  // eager runtime.
  if (enable_grappler) {
    tensorflow::ConfigProto config;
    // Do not skip grappler optimization even for small graphs.
    config.mutable_graph_options()

 * article on <a href="https://github.com/google/guava/wiki/ListenableFutureExplained">{@code
 * ListenableFuture}</a>.
 *
 * <p>The main purpose of {@code ListenableFuture} is to help you chain together a graph of
 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, Function, Executor) Futures.transform}, but you will often

 * article on <a href="https://github.com/google/guava/wiki/ListenableFutureExplained">{@code
 * ListenableFuture}</a>.
 *
 * <p>The main purpose of {@code ListenableFuture} is to help you chain together a graph of
 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, Function, Executor) Futures.transform}, but you will often

func (c *typeConv) Type(dtype dwarf.Type, pos token.Pos) *Type {
	return c.loadType(dtype, pos, "")
}

// loadType recursively loads the requested dtype and its dependency graph.
func (c *typeConv) loadType(dtype dwarf.Type, pos token.Pos, parent string) *Type {
	// Always recompute bad pointer typedefs, as the set of such
	// typedefs changes as we see more types.
	checkCache := true