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.

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s);

// Returns a sorted vector of std::pair<function_name, gradient_func> from
// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def);

// Returns a sorted vector of names contained in `grad_def`

      ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_) {
    lru_list_.push_front(key);
    Entry entry{timer_seconds_(), value, lru_list_.begin()};
    auto insert = cache_.insert(std::make_pair(key, entry));
    if (!insert.second) {
      lru_list_.erase(insert.first->second.lru_iterator);
      insert.first->second = entry;
    } else if (max_entries_ > 0 && cache_.size() > max_entries_) {

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