EXPECT_EQ(feed, add_in_0.oper);
  EXPECT_EQ(0, add_in_0.index);
  TF_Output add_in_1 = TF_OperationInput(TF_Input{add, 1});
  EXPECT_EQ(three, add_in_1.oper);
  EXPECT_EQ(0, add_in_1.index);
  EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{add, 0}));
  EXPECT_EQ(0, TF_OperationNumControlInputs(add));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(add));

        return;
      }
      // We have a convoluted scheme here: Using the C++ graph construction API
      // to add potentially many nodes to the graph without running the checks
      // (such as uniqueness of the names of nodes) we run with other functions
      // that add a node to the graph (like TF_FinishOperation).
      if (!g->name_map.insert(std::make_pair(n->name(), n)).second) {

    *   Add batch_dims argument to tf.gather.
    *   Add support for `add_metric` in the graph function mode.
    *   Add C++ Gradient for BatchMatMulV2.
    *   Added tf.random.binomial
    *   Added gradient for SparseToDense op.
    *   Add legacy string flat hash map op kernels
    *   Add a ragged size op and register it to the op dispatcher
    *   Add broadcasting support to tf.matmul.

      static_cast<void*>(gauge->gauge->GetCell(label1, label2)));
}

void TFE_MonitoringSamplerCellAdd(TFE_MonitoringSamplerCell* cell,
                                  double value) {
  cell->cell.Add(value);
}

void TFE_MonitoringSamplerCellValue(TFE_MonitoringSamplerCell* cell,
                                    TF_Buffer* buf) {
  string content;
  cell->cell.value().SerializeToString(&content);

      )
  )
  return var


def choose_compiler_Win(environ_cp):
  question = 'Do you want to use Clang to build TensorFlow?'
  yes_reply = 'Add "--config=win_clang" to compile TensorFlow with CLANG.'
  no_reply = 'MSVC will be used to compile TensorFlow.'
  var = int(
      get_var(
          environ_cp, 'TF_NEED_CLANG', None, True, question, yes_reply, no_reply

  NodeDef node_def;
  tensorflow::ImmediateExecutionOperation* op = tensorflow::unwrap(tfe_op);
  node_def.set_name(op->Name());
  node_def.set_op(op->Name());
  for (int i = 0; i < num_inputs; ++i) {
    node_def.add_input("dummy_input");
  }
  OperationFromInterface(op)->Attrs().FillAttrValueMap(node_def.mutable_attr());

  const tensorflow::OpRegistrationData* op_reg_data;
  status->status =

build:linux --copt="-Wno-deprecated-declarations"
build:linux --copt="-Wno-ignored-attributes"
build:linux --copt="-Wno-array-bounds"

# Add unused-result as an error on Linux.
build:linux --copt="-Wunused-result"
build:linux --copt="-Werror=unused-result"
# Add switch as an error on Linux.
build:linux --copt="-Wswitch"
build:linux --copt="-Werror=switch"
# Required for building with clang

  if (num_opers == -1) {
    for (const Node* node : fn_body->graph.op_nodes()) {
      const auto& iter = input_nodes.find(node);
      if (iter == input_nodes.end()) {
        // This node is not referenced in inputs. Add it to the body.
        body_nodes->push_back(node);
      } else {
        // This node is referenced in inputs. Currently, we place an
        // artificial restriction and require that when num_opers=-1, such