-  extern __typeof (name) aliasname __attribute__ ((weak, alias (#name)));
+  extern __typeof (name) aliasname __attribute__ ((weak, alias (#name))) \
+    __attribute_copy__ (name);
 
 /* Same as WEAK_ALIAS, but mark symbol as hidden.  */
 # define weak_hidden_alias(name, aliasname) \
   _weak_hidden_alias (name, aliasname)
 # define _weak_hidden_alias(name, aliasname) \
   extern __typeof (name) aliasname \

}

}  // end extern "C"

namespace tensorflow {

void RecordMutation(TF_Graph* graph, const TF_Operation& op,
                    const char* mutation_type) {
  // If any session has already run this node_id, mark this session as
  // unrunnable.
  for (auto it : graph->sessions) {
    mutex_lock session_lock(it.first->mu);
    if (it.first->last_num_graph_nodes > op.node.id()) {
      it.second = strings::StrCat(

            with `mask_zero=True` to automatically infer a correct padding mask.
        *   Added a `use_causal_mask` call time argument to the layer. Passing
            `use_causal_mask=True` will compute a causal attention mask, and
            optionally combine it with any `attention_mask` passed in directly
            when calling the layer.

namespace {

using ::tensorflow::string;

void ReplaceTaskInServerDef(tensorflow::ServerDef* server_def, int task_index) {
  tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat("localhost:", port);
}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  std::vector<std::string> devices{
      "/job:localhost/replica:0/task:0/device:CPU:0",
      "/job:localhost/replica:0/task:0/device:CPU:1"};
  ParallelDevice parallel_device(std::move(devices));
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> handle_op(

namespace {

using ::tensorflow::string;

void TestRemoteExecute(bool async) {
  tensorflow::ServerDef server_def = GetServerDef(2);

  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();

  server_def.set_task_index(1);

  std::unique_ptr<tensorflow::GrpcServer> worker_server;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

    ask_for_var: string for how to ask for user input.
    var_default: default value string.

  Returns:
    string value for var_name
  """
  var = environ_cp.get(var_name)
  if not var:
    var = get_input(ask_for_var)
    print('\n')
  if not var:
    var = var_default
  return var


def prompt_loop_or_load_from_env(environ_cp,

    TFE_Context* ctx, const char* task, int64_t timeout_in_ms,
    TF_Status* status) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {

// multi worker collective ops to detect failures in the cluster.  If a peer is
// down, collective ops may hang.
TF_CAPI_EXPORT extern void TFE_CollectiveOpsCheckPeerHealth(
    TFE_Context* ctx, const char* task, int64_t timeout_in_ms,
    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;

// gradients available initially).
//
// Then we repeatedly pop an entry from the stack, run its backprop, and update
// the gradients of its inputs. Once we have computed all gradients for a single
// input we can mark this input as done, and this can trigger adding an entry to
// the stack if all outputs of that entry are now done.
//
// When the stack is empty we have gradients for all tensors we're interested
// in.

namespace {