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");
  }

  TFE_OpAddAttrs(op, attributes);
  TFE_OpSetDevice(op, dev->underlying_device.c_str(), s);
  if (TF_GetCode(s) != TF_OK) return;
  int num_inputs = TFE_OpGetFlatInputCount(original_op, s);
  if (TF_GetCode(s) != TF_OK) return;
  for (int j = 0; j < num_inputs; ++j) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;

               absl::StatusMessageAsCStr(status));
}

void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s) {
  for (int i = 0; i < num_inputs; i++) {
    tsl::Set_TF_Status_from_Status(s, unwrap(op)->AddInput(unwrap(inputs[i])));
    if (TF_GetCode(s) != TF_OK) {
      return;

      reinterpret_cast<NamedParallelDevice*>(device_info);
  std::vector<MaybeParallelTensorUnowned> typed_inputs;
  int num_inputs = TFE_OpGetFlatInputCount(original_op, status);
  if (TF_GetCode(status) != TF_OK) return;
  typed_inputs.reserve(num_inputs);
  for (int i = 0; i < num_inputs; ++i) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, i, status);
    if (TF_GetCode(status) != TF_OK) return;

// returned through the provided TF_OutputList.
// Any active tape will observe the effects of this execution.
void TF_ExecuteOperation(TF_AbstractOp* op, int num_inputs,
                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s);

// Creates a new TF_AbstractFunction from the current tracing states in the

TF_CAPI_EXPORT extern void TFE_OpAddInputList(TFE_Op* op,
                                              TFE_TensorHandle** inputs,
                                              int num_inputs,
                                              TF_Status* status);

// Fetches the current number of inputs attached to `op`.
//
// Does not use the operation's definition to determine how many inputs should

}

void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs,
                     int num_inputs) {
  std::vector<NodeBuilder::NodeOut> input_list;
  input_list.reserve(num_inputs);
  for (int i = 0; i < num_inputs; ++i) {
    input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
  }
  desc->node_builder.Input(input_list);
}

}

void TFE_OpAddInputList(TFE_Op* op, TFE_TensorHandle** inputs, int num_inputs,
                        TF_Status* status) {
  status->status = tensorflow::unwrap(op)->AddInputList(
      {reinterpret_cast<tensorflow::AbstractTensorHandle**>(
           tensorflow::unwrap(inputs)),
       static_cast<size_t>(num_inputs)});
}

extern int TFE_OpGetFlatInputCount(const TFE_Op* op, TF_Status* status) {

// For inputs that take a list of tensors.
// inputs must point to TF_Output[num_inputs].
TF_CAPI_EXPORT extern void TF_AddInputList(TF_OperationDescription* desc,
                                           const TF_Output* inputs,
                                           int num_inputs);

// Call once per control input to `desc`.

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddAttrs(ret, TFE_OpGetAttrs(other));
  int num_inputs = TFE_OpGetFlatInputCount(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  for (int input_index = 0; input_index < num_inputs; ++input_index) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(other, input_index, status);