input_tensors.empty()
                        ? nullptr
                        : const_cast<TF_Tensor**>(input_tensors.data()),
                    /*input_tensors_as_shapes*/ nullptr,
                    /*input_resource_shapes_and_types*/ nullptr, &output_shapes,
                    /*output_resource_shapes_and_types*/ nullptr, status_);
    CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);

      inputs, noutputs, outputs, output_names, 0, nullptr, nullptr, opts,
      description, status);
}

const char* TF_FunctionName(TF_Function* func) {
  return func->record->fdef().signature().name().c_str();
}

void TF_GraphCopyFunction(TF_Graph* g, const TF_Function* func,
                          const TF_Function* grad, TF_Status* status) {
  if (func == nullptr) {
    status->status = InvalidArgument(

  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters
                /*outputs*/ nullptr, /*output_values*/ nullptr, /*noutputs*/ 0,
                /*targets*/ &enqueue_op, /*ntargets*/ 1,
                /*run_metadata*/ nullptr, status);
  VLOG(1) << "Enqueuing is done.";
}

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

CheckpointReader::CheckpointReader(const string& filename, TF_Status* status)
    : reader_(nullptr),
      v2_reader_(nullptr),
      var_to_shape_map_(nullptr),
      var_to_data_type_map_(nullptr) {
  // Depending on whether this is a V2 ckpt, initializes "reader_" or
  // "v2_reader_".
  std::vector<string> v2_path;

  output_values_.resize(outputs_.size(), nullptr);

  const TF_Output* inputs_ptr = inputs_.empty() ? nullptr : &inputs_[0];
  TF_Tensor* const* input_values_ptr =
      input_values_.empty() ? nullptr : &input_values_[0];

  const TF_Output* outputs_ptr = outputs_.empty() ? nullptr : &outputs_[0];
  TF_Tensor** output_values_ptr =
      output_values_.empty() ? nullptr : &output_values_[0];

  delete[] params->body_inputs;
  delete[] params->body_outputs;
}

TF_WhileParams EmptyWhileParams() {
  return {0,       nullptr, nullptr, {nullptr, 0},
          nullptr, nullptr, nullptr, nullptr};
}

}  // namespace

TF_WhileParams TF_NewWhile(TF_Graph* g, TF_Output* inputs, int ninputs,
                           TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)

  TF_Status* status = TF_NewStatus();

  TF_DeleteStatus(nullptr);
  TF_DeleteBuffer(nullptr);
  TF_DeleteTensor(nullptr);
  TF_DeleteSessionOptions(nullptr);
  TF_DeleteGraph(nullptr);
  TF_DeleteImportGraphDefOptions(nullptr);
  TF_DeleteImportGraphDefResults(nullptr);
  TF_DeleteFunction(nullptr);
  TF_DeleteSession(nullptr, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  func_ = TF_GraphToFunction(
      func_graph.get(), "func", /*append_hash_to_fn_name=*/false, -1,
      /*opers=*/nullptr, 0, nullptr, 3, outputs,
      /*output_names=*/nullptr,
      /*opts=*/nullptr, /*description=*/nullptr, s.get());
  ASSERT_EQ(TF_OK, TF_GetCode(s.get())) << TF_Message(s.get());
  ASSERT_NE(func_, nullptr);

  // Verify that FunctionDef has 2 attributes, "v1" and "v2".

 private:
  const AbstractContextKind kind_;
};

namespace internal {
struct AbstractContextDeleter {
  void operator()(AbstractContext* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using AbstractContextPtr =
    std::unique_ptr<AbstractContext, internal::AbstractContextDeleter>;

}  // namespace tensorflow

// Returns the operation in the graph with `oper_name`. Returns nullptr if
// no operation found.
TF_CAPI_EXPORT extern TF_Operation* TF_GraphOperationByName(
    TF_Graph* graph, const char* oper_name);

// Iterate through the operations of a graph.  To use:
// size_t pos = 0;
// TF_Operation* oper;
// while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
//   DoSomethingWithOperation(oper);
// }