code: def test_positional_args(self):
      # Test positional check on input type
      class InputPositional(Typing):
          @property
          def input_types(self):
              return {"x": NeuralType(("B",), ElementType())}
  
          @typecheck()
          def __call__(self, x):
              x += 1
              return x
  
      obj = InputPositional()
  
      with pytest.raises(TypeError):
          _ = obj(torch.zeros(10))
  
      # Test positional pass-through for only output ports defined
      # NOTE: This is required behaviour to support type checking of NeMo Dataset class
      # during collate_fn() call.
      class OutputPositionalPassthrough(Typing):
          @property
          def output_types(self):
              return {"y": NeuralType(("B",), ElementType())}
  
          @typecheck()
          def __call__(self, x):
              x += 1
              return x
  
      obj = OutputPositionalPassthrough()
      result = obj(torch.zeros(10))
  
      assert result.sum() == torch.tensor(10.0)
  
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 15 for calling the TestNeuralTypeCheckSystem obj's underlying member method code with example usage: obj.test_positional_args() and returns: dict, x, dict, x