// Execute the operation defined by 'op' and return handles to computed
// tensors in `retvals`.
//
// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and
// '*num_retvals' should be set to the size of this array. It is an error if
// the size of 'retvals' is less than the number of outputs. This call sets
// *num_retvals to the number of outputs.
//

	if err != nil {
		return nil, err
	}
	retval := make([]*v1alpha1.IstioOperator, 0)
	for _, un := range ul.Items {
		fixTimestampRelatedUnmarshalIssues(&un)
		by := util.ToYAML(un.Object)
		iop, err := operator_istio.UnmarshalIstioOperator(by, true)
		if err != nil {
			return nil, err
		}
		retval = append(retval, iop)
	}
	return retval, nil
}

func decodeYAML(data []byte) (*unstructured.Unstructured, error) {
	retval := &unstructured.Unstructured{Object: map[string]interface{}{}}
	// ensure this isn't JSON
	if json.Unmarshal(data, &retval.Object) == nil {
		return nil, fmt.Errorf("data is JSON, not YAML: %s", string(data))
	}
	// ensure it is YAML
	retval.Object = map[string]interface{}{}
	if err := yaml.Unmarshal(data, &retval.Object); err != nil {

          bytes.length);
      return padToLong();
    }

    @Override
    public long padToLong() {
      long retVal = (bytes[0] & 0xFF);
      for (int i = 1; i < Math.min(bytes.length, 8); i++) {
        retVal |= (bytes[i] & 0xFFL) << (i * 8);
      }
      return retVal;
    }

    @Override
    void writeBytesToImpl(byte[] dest, int offset, int maxLength) {

	var retval []certificates.CertificateSigningRequestCondition
	for i, c := range csr.Status.Conditions {
		if c.Type == conditionType {
			retval = append(retval, csr.Status.Conditions[i])
		}
	}
	return retval
}

// getValidationOptions returns the validation options to be

    if (mul) {
      auto c = ops::Mul(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    } else {
      auto c = ops::Add(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    }

          bytes.length);
      return padToLong();
    }

    @Override
    public long padToLong() {
      long retVal = (bytes[0] & 0xFF);
      for (int i = 1; i < Math.min(bytes.length, 8); i++) {
        retVal |= (bytes[i] & 0xFFL) << (i * 8);
      }
      return retVal;
    }

    @Override
    void writeBytesToImpl(byte[] dest, int offset, int maxLength) {

                             int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);

      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }
  Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                 int* num_retvals) override {
    auto* tf_opdesc = op_.release();
    if (tf_opdesc == nullptr) {
      return errors::InvalidArgument("AbstractOp is incomplete.");
    }

}

Status MlirAbstractOp::Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) {
  OperationState* state;
  TF_RETURN_IF_ERROR(Create(operands_, &state));
  Operation* op = function_context_->CreateOperationFromState(*state);
  *num_retvals = op->getNumResults();
  for (int i = 0; i < *num_retvals; i++)
    retvals[i] = new MlirTensor(op->getResult(i));
  return absl::OkStatus();
}