(*f.mutable_attr())[attr.first] = attr.second;
  }

  std::vector<Output> func_inputs;
  std::vector<DataType> input_dtypes;
  const int num_inputs = op.num_inputs();
  func_inputs.reserve(num_inputs + grad_inputs.size());
  input_dtypes.reserve(num_inputs);

  for (int i = 0; i < num_inputs; i++) {
    func_inputs.push_back(op.input(i));
    input_dtypes.push_back(op.input_type(i));
  }

// TF_Value* TF_ExecuteFunction(TFE_Context*, TF_ConcreteFunction*, TF_Value*
// inputs, int num_inputs, TF_Status* status);
TF_CAPI_EXPORT extern TFE_Op* TF_ConcreteFunctionMakeCallOp(
    TF_ConcreteFunction* func, TFE_TensorHandle** inputs, int num_inputs,
    TF_Status* status);

#ifdef __cplusplus
}  // end extern "C"
#endif  // __cplusplus

                                      TFE_TensorHandle** inputs, int num_inputs,
                                      TF_Status* status) {
  tensorflow::ImmediateOpPtr call_op;
  absl::Span<tensorflow::AbstractTensorHandle* const> input_span(
      reinterpret_cast<tensorflow::AbstractTensorHandle**>(
          tensorflow::unwrap(inputs)),
      static_cast<size_t>(num_inputs));

                                          int num_inputs, TF_Status* status) {
  tensorflow::ImmediateOpPtr call_op;
  absl::Span<tensorflow::AbstractTensorHandle* const> input_span(
      reinterpret_cast<tensorflow::AbstractTensorHandle**>(
          tensorflow::unwrap(inputs)),
      static_cast<size_t>(num_inputs));
  status->status = tensorflow::unwrap(func)->MakeCallOp(input_span, &call_op);

    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),
                    /*use_function=*/use_function);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  for (int i = 0; i < num_inputs; ++i) {
    if (!outputs[i]) continue;

namespace tensorflow {

Operation::Operation(Node* n) : inputs_(GetInputs(n)), node_(n) {}

Output Operation::input(int32_t i) const {
  CHECK_NOTNULL(node_);
  CHECK_GE(i, 0);
  CHECK_LT(i, node_->num_inputs());
  // Handle the case where the input was unknown at the time this
  // Operation was constructed.
  if (inputs_[i].first == nullptr && inputs_[i].second == -1) {
    for (const Edge* e : node_->in_edges()) {

  const Node* identity_node = node_name_index.at("identity_op");
  EXPECT_EQ(identity_node->op_def().name(), "Identity");
  EXPECT_EQ(identity_node->attrs().FindByString("T")->type(), DT_FLOAT);

  EXPECT_EQ(identity_node->num_inputs(), 1);
  const Node* identity_input_node = nullptr;
  TF_EXPECT_OK(identity_node->input_node(0, &identity_input_node));
  EXPECT_EQ(identity_input_node->name(), "_arg0");

  using tensorflow::shape_inference::ShapeHandle;

  const int num_inputs = input_shapes->num_items;
  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");
  }

    if (data_type.getIntOrFloatBitWidth() == 64) continue;

    const auto& block_arg = replicate.GetBody().getArgument(replicate_arg);

    int64_t num_inputs = 0;
    if (replicate.IsReplicatedBlockArgument(block_arg)) {
      num_inputs = num_replicas;
    } else {
      num_inputs = 1;
    }

    // We have found a mirrored variable which is an input to the replicated

namespace tensorflow {

namespace {

class WhileLoopTest : public ::testing::Test {
 protected:
  WhileLoopTest() : scope_(Scope::NewRootScope()) {}

  void Init(int num_inputs, DataType dtype = DT_INT32) {
    for (int i = 0; i < num_inputs; ++i) {
      inputs_.push_back(ops::Placeholder(scope_, dtype));
    }
  }

  void CreateLoop(const ops::CondGraphBuilderFn& cond,