absl::Span<AbstractTensorHandle*> retvals, int* num_retvals,
               ForwardOperation* forward_op_, Tape* tape,
               const GradientRegistry& registry) {
  TF_RETURN_IF_ERROR(op_->Execute(retvals, num_retvals));
  for (int i = 0; i < *num_retvals; i++) {
    // TODO(srbs): Manage refcount of ForwardOperation's inputs/outputs.
    forward_op_->outputs.push_back(retvals[i]);
  }

			HTTPGet: &apimirror.HTTPGetAction{
				Path: "/hello/sunnyvale",
				Port: apimirror.IntOrString{IntVal: int32(appPort)},
			},
		},
		"/app-health/hello-world/livez": &Prober{
			HTTPGet: &apimirror.HTTPGetAction{
				Port: apimirror.IntOrString{IntVal: int32(appPort)},
			},
		},
	}
	simpleTCPConfig := KubeAppProbers{
		"/app-health/hello-world/readyz": &Prober{

								Protocol: v1.ProtocolUDP,
								TargetPort: intstr.IntOrString{
									Type:   0,
									IntVal: 53,
								},
							},
							{
								Name:     "dns-tcp",
								Port:     53,
								Protocol: v1.ProtocolTCP,
								TargetPort: intstr.IntOrString{
									Type:   0,
									IntVal: 53,
								},
							},
						},
						Selector: map[string]string{

	switch rval := result.(type) {
	case string:
		return FromString(rval), nil
	case float64:
		return FromFloat(rval), nil
	case int64:
		return FromInt(rval), nil
	case uint64:
		if rval <= math.MaxInt64 {
			return FromInt(int64(rval)), nil
		}
		return FromFloat(float64(rval)), nil
	case bool:
		return FromBool(rval), nil
	case jstream.KVS:

                             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();
}

            url_ = url;
        }

        /**
         * @return Returns the cal.
         */
        public Calendar getCal() {
            return cal;
        }

        /**
         * @param cal
         *            The cal to set.
         */
        public void setCal(final Calendar cal) {
            this.cal = cal;
        }
    }

    /**
     *
     */

    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()));
    }

    return op.emitOpError(
        llvm::formatv(kBadArrayAttrLengthMsg, tensorflow::kOutputShardingAttr,
                      op.getNumResults(), output_shardings.size()));

  // Set retvals metadata in proto.
  for (auto output_sharding_and_idx : llvm::enumerate(output_shardings))
    if (failed(SetOpSharding(op, output_sharding_and_idx.value(),
                             tensorflow::kOutputShardingAttr,