for (int i = 0; i < inputs.size(); i++) {
      GraphTensor* t = dyn_cast<GraphTensor>(inputs[i]);
      if (!t) {
        return tensorflow::errors::InvalidArgument(
            "Unable to cast input to GraphTensor");
      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }

  // The first one will the function's output
  std::vector<TF_Output> outputs;

  // Add while loop to func_graph_
  {
    // The inputs to the while loop
    std::vector<TF_Output> inputs = {{feed1, 0}, {feed2, 0}};
    std::unique_ptr<TF_WhileParams> params(new TF_WhileParams(
        TF_NewWhile(func_graph_, &inputs[0], inputs.size(), s_)));

  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_DeleteStatus(s);
}

void CSession::SetInputs(
    std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs) {
  DeleteInputValues();
  inputs_.clear();
  for (const auto& p : inputs) {
    inputs_.emplace_back(TF_Output{p.first, 0});
    input_values_.emplace_back(p.second);
  }
}

void CSession::SetOutputs(std::initializer_list<TF_Operation*> outputs) {

       * could actually hurt in some cases, as it forces us to keep all inputs in memory until the
       * final input completes.
       */
      @RetainedLocalRef
      ImmutableCollection<? extends ListenableFuture<? extends InputT>> localFutures = futures;
      ImmutableCollection<? extends Future<? extends InputT>> localFuturesOrNull =
          collectsValues ? localFutures : null;

            final VaErrorHook hook) {
        return getEntity(form).map(entity -> {
            final String[] newInputs = splitLine(form.inputs);
            validateSynonymString(action, newInputs, "inputs", hook);
            entity.setNewInputs(newInputs);
            final String[] newOutputs = splitLine(form.outputs);
            validateSynonymString(action, newOutputs, "outputs", hook);

public class ProtwordsItem extends DictionaryItem {
    private final String input;

    private String newInput;

    public ProtwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {
            // create
            newInput = input;
        }
    }

    public String getNewInput() {
        return newInput;
    }

public class StopwordsItem extends DictionaryItem {
    private final String input;

    private String newInput;

    public StopwordsItem(final long id, final String input) {
        this.id = id;
        this.input = input;

        if (id == 0) {
            // create
            newInput = input;
        }
    }

    public String getNewInput() {
        return newInput;
    }

            if (Arrays.equals(newInputs, newOutputs)) {
                return StringUtils.join(newInputs, ",");
            }
            return StringUtils.join(newInputs, ",") + "=>" + StringUtils.join(newOutputs, ",");
        }
        if (Arrays.equals(inputs, outputs)) {
            return StringUtils.join(inputs, ",");
        }
        return StringUtils.join(inputs, ",") + "=>" + StringUtils.join(outputs, ",");

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors
TF_Operation* Split3(TF_Operation* input, TF_Graph* graph, TF_Status* s,
                     const char* name = "split3");

bool IsPlaceholder(const tensorflow::NodeDef& node_def);

          A result(Collector<T, A, R> collector, Iterable<T> inputs) {
        List<A> stack = new ArrayList<>();
        for (T input : inputs) {
          A newAccum = collector.supplier().get();
          collector.accumulator().accept(newAccum, input);
          push(stack, newAccum);
        }
        push(stack, collector.supplier().get());