code: def test_output_type_override(self):
      class OutputTypes(Typing):
          @property
          def output_types(self):
              return {"y": NeuralType(("B",), ElementType())}
  
          @typecheck()
          def __call__(self, x):
              x += 1
              return x
  
          @typecheck(
              output_types={
                  "z": NeuralType(("B",), CategoricalValuesType())
              }
          )
          def forward(self, z):
              z -= 1
              return z
  
      obj = OutputTypes()
      result = obj(x=torch.zeros(10))
  
      assert result.sum() == torch.tensor(10.0)
      assert (
          result.neural_type.compare(NeuralType(("B",), ElementType()))
          == NeuralTypeComparisonResult.SAME
      )
  
      # Test passing positional args
      # Positional args allowed if input types is not set !
      result = obj(torch.zeros(10))
      assert result.sum() == torch.tensor(10.0)
  
      # Test override
      result2 = obj.forward(z=torch.zeros(10))
  
      assert result2.sum() == torch.tensor(-10.0)
      assert hasattr(result2, "neural_type")
      assert (
          result2.neural_type.compare(
              NeuralType(("B",), CategoricalValuesType())
          )
          == 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 23 for calling the TestNeuralTypeCheckSystem obj's underlying member method code with example usage: obj.test_output_type_override() and returns: dict, x, z