}

      LIST_CASE(
          s, TF_ATTR_STRING, metadata.total_size = 0;
          for (int i = 0; i < attr->list().s_size();
               ++i) { metadata.total_size += attr->list().s(i).size(); });
      LIST_CASE(i, TF_ATTR_INT);
      LIST_CASE(f, TF_ATTR_FLOAT);
      LIST_CASE(b, TF_ATTR_BOOL);
      LIST_CASE(type, TF_ATTR_TYPE);
      LIST_CASE(

                                 TF_DataType dtype) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape_and_type = shape_list->items[index];
  shape_and_type.dtype = dtype;
}

void TF_DeleteShapeAndTypeList(TF_ShapeAndTypeList* shape_list) {
  if (shape_list == nullptr) return;
  for (size_t i = 0; i < shape_list->num_items; ++i) {

    if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) {
      tsl::Set_TF_Status_from_Status(
          status,
          absl::InvalidArgumentError(
              "Not enough storage to hold the requested list of nodes"));
      return;
    }
    memcpy(values[index], s.data(), s.size());
    p += s.size();
    index++;
  }
}

void TF_GetFetchNodesListSize(const TF_GrapplerItem* item, int* num_values,

  ~TapeVSpace() override {}

  // Returns the number of elements in the gradient tensor.
  int64_t NumElements(AbstractTensorHandle* tensor) const override;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  AbstractTensorHandle* AggregateGradients(
      gtl::ArraySlice<AbstractTensorHandle*> gradient_tensors) const override;

  // Calls the passed-in backward function.

  std::unordered_set<string> nodes_preserved = item.NodesToPreserve();
  TF_GrapplerItem* c_item = reinterpret_cast<TF_GrapplerItem*>(&item);

  int list_total_size = 0;
  for (const string& s : nodes_preserved) {
    list_total_size += s.size();
  }

  size_t storage_size = 0;
  int num_values = 0;
  TF_Status* status = TF_NewStatus();

      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {
        return InvalidArgument("TF_Output ", node->name(), ":", idx,
                               " appears more than once in the input list");
      }
      indices.push_back(idx);
    }
  }
  return absl::OkStatus();
}

// Converts `noutputs` and `outputs` into `outputs_tensors` and does various
// checks while doing so.

      }
      AbstractFunction* func = nullptr;
      OutputList output_list;
      output_list.expected_num_outputs = 0;
      output_list.outputs.reserve(outputs.size());
      for (int i = 0; i < model_outputs.size(); i++) {
        if (model_outputs[i]) {
          output_list.outputs.emplace_back(model_outputs[i]);
          output_list.expected_num_outputs += 1;
        } else {
          null_indices.insert(i);
        }

        }
        TFE_OpSetAttrTypeList(
            op, attr_name,
            reinterpret_cast<const TF_DataType*>(type_vector.data()),
            type_size);
      }

      // Rest are not supported.
      if (default_value.list().shape_size() > 0 ||
          default_value.list().func_size() > 0 ||
          default_value.list().tensor_size() > 0) {

TEST_F(CApiAttributesTest, StringList) {
  std::vector<string> list = {"bugs", "bunny", "duck"};
  std::unique_ptr<const void*[]> list_ptrs;
  std::unique_ptr<size_t[]> list_lens;
  StringVectorToArrays(list, &list_ptrs, &list_lens);
  int list_total_size = 0;
  for (const auto& s : list) {
    list_total_size += s.size();
  }

  auto desc = init("list(string)");

auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.
class GraphTensor : public TracingTensorHandle {
 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}