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;
    }

    private final String[] inputs;

    private final String output;

    private String[] newInputs;

    private String newOutput;

    public CharMappingItem(final long id, final String[] inputs, final String output) {
        this.id = id;
        this.inputs = inputs;
        this.output = output == null ? null : output.replace("\n", " ");
        Arrays.sort(inputs);

        if (id == 0) {

            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, ",");

    // Create loop: while (input1 < input2) input1 += input2 + 1
    TF_Operation* less_than = LessThan(
        params->cond_inputs[0], params->cond_inputs[1], params->cond_graph, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    params->cond_output = {less_than, 0};

    TF_Operation* add1 = Add(params->body_inputs[0], params->body_inputs[1],

    return result;
  }
  std::vector<ParallelTensor*> parallel_inputs;
  std::vector<std::unique_ptr<ParallelTensor>> implicitly_broadcast_tensors;
  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {

      : forward_inputs_(f_inputs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(vnvo2409): Add control dependency

// to the passed operator
func checkPolicyCond(op string, input1, input2 string) bool {
	switch op {
	case policyCondEqual:
		return input1 == input2
	case policyCondStartsWith:
		return strings.HasPrefix(input1, input2)
	}
	return false
}

// checkPostPolicy - apply policy conditions and validate input values.
// (http://docs.aws.amazon.com/AmazonS3/latest/API/sigv4-HTTPPOSTConstructPolicy.html)

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build inputs and outputs.
  TF_AbstractTensor* inputs[2] = {at, at};
  TF_OutputList* o = TF_NewOutputList();
  TF_OutputListSetNumOutputs(o, 1, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Execute.
  TF_ExecuteOperation(op, 2, inputs, o, status.get());

    std::vector<TFE_TensorHandle*> device_inputs;
    device_inputs.reserve(inputs.size());
    for (int input_index = 0; input_index < inputs.size(); ++input_index) {
      // Parallel tensors are divided between operations by device.
      device_inputs.push_back(inputs[input_index]->tensor(device_index));
    }
    device_thread->StartExecute(
        context, operation_name, std::move(device_inputs), attributes,

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,