code: def test_multiple_mixed_output_types_only(self):
      class MultipleMixedOutputTypes(Typing):
          @property
          def output_types(self):
              return {
                  "y": NeuralType(("B",), ElementType()),
                  "z": [NeuralType(("B",), ElementType())],
              }
  
          @typecheck()
          def __call__(self, x):
              y = x + 1
              z = x + 2
              return y, [z, z]
  
      obj = MultipleMixedOutputTypes()
      result_y, result_z = obj(x=torch.zeros(10))
  
      assert result_y.sum() == torch.tensor(10.0)
      assert (
          result_y.neural_type.compare(
              NeuralType(("B",), ElementType())
          )
          == NeuralTypeComparisonResult.SAME
      )
  
      assert result_z[0].sum() == torch.tensor(20.0)
      assert (
          result_z[0].neural_type.compare(
              NeuralType(("B",), ElementType())
          )
          == NeuralTypeComparisonResult.SAME
      )
  
      assert result_z[1].sum() == torch.tensor(20.0)
      assert (
          result_z[1].neural_type.compare(
              NeuralType(("B",), ElementType())
          )
          == NeuralTypeComparisonResult.SAME
      )
  
imports: from typing import List, NamedTuple
  import pytest
  import torch
  from nemo.core import Typing, typecheck
  from nemo.core.neural_types import *
label_desc: Use image node_id 8 for calling the TestNeuralTypeCheckSystem obj's underlying member method code with example usage: obj.test_multiple_mixed_output_types_only() and returns: dict, y,  list