if (!status.ok()) out_tensor->reset();
    }
  }
  if (!status.ok()) {
    tsl::Set_TF_Status_from_Status(out_status, status);
  }
}

std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
          std::unique_ptr<TensorSliceReader::VarToDataTypeMap>>
CheckpointReader::BuildV2VarMaps() {
  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
            std::unique_ptr<TensorSliceReader::VarToDataTypeMap> >
  BuildV2VarMaps();

  // Invariant: exactly one of "reader_" and "v2_reader_" is non-null.

  // users want to compute multiple gradients over the same tape.
  explicit GradientTape(bool persistent) : persistent_(persistent) {}
  ~GradientTape() {
    for (const auto& pair : op_tape_) {
      pair.second.backward_function_deleter(pair.second.backward_function);
    }
  }

  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.

}

static bool TF_Run_Inputs(TF_Tensor* const* c_inputs,
                          std::vector<std::pair<string, Tensor>>* input_pairs,
                          TF_Status* status) {
  const int ninputs = input_pairs->size();
  for (int i = 0; i < ninputs; ++i) {
    status->status =
        TF_TensorToTensorV1(c_inputs[i], &(*input_pairs)[i].second);
    if (!status->status.ok()) return false;
  }
  return true;
}

//      output tensor naming, but it the index is actually an input index)
struct EdgeSpec : public std::pair<string, string> {
  typedef std::pair<string, string> Base;

  // Inherit the set of constructors
  using Base::pair;

  string ToString() const { return strings::StrCat(first, "->", second); }
};

class CApiFunctionTest : public ::testing::Test {
 protected:

    int64_t target_id = target_tensor_ids[i];
    if (sources_set.find(target_id) != sources_set.end()) {
      auto tensor = targets[i];
      sources_that_are_targets.insert(
          std::make_pair(target_id, TapeTensor(tensor)));
    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,

//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_ops_2
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py

        (`tf.float32`).
*   NNAPI
    *   Adds NNAPI Delegation support for requantization use cases by converting
        the operation into a dequantize-quantize pair.
    *   Removes deprecated `Interpreter.setUseNNAPI(boolean)` Java API. Use
        `Interpreter.Options.setUseNNAPI` instead.
    *   Deprecates `Interpreter::UseNNAPI(bool)` C++ API. Use `NnApiDelegate()`

//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py

// Sets `output` based on `proto`, which should be a serialized
// CppShapeInferenceResult::HandleData proto. `output` should be a resource
// or variant tensor.
// NOTE(skyewm): `proto` is passed a void*/size_t pair instead of a std::string
// because I couldn't get SWIG to work otherwise.
TF_CAPI_EXPORT extern void TF_SetHandleShapeAndType(TF_Graph* graph,
                                                    TF_Output output,