code: def test_input_output_neural_types(self):
      class NodeA(Typing):
          @property
          def input_types(self):
              return {"x": NeuralType(("B",), ElementType())}
  
          @property
          def output_types(self):
              return {"y": NeuralType(("B", "D"), LogitsType())}
  
          @typecheck()
          def __call__(self, x):
              y = torch.randn(x.shape[0], 4)
              return y
  
      class NodeB(Typing):
          @property
          def input_types(self):
              return {"w": NeuralType(("B", "D"), LogitsType())}
  
          @property
          def output_types(self):
              return {"u": NeuralType(("B",), LabelsType())}
  
          @typecheck()
          def __call__(self, w):
              _, u = w.max(-1)
              return u
  
      nodeA = NodeA()
      nodeB = NodeB()
  
      outA = nodeA(x=torch.zeros(10))
      outB = nodeB(w=outA)
  
      assert outB.shape == torch.Size([10])
      assert (
          outB.neural_type.compare(NeuralType(("B",), LabelsType()))
          == 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 17 for calling the TestNeuralTypeCheckSystem obj's underlying member method code with example usage: obj.test_input_output_neural_types() and returns: dict, dict, y, dict, dict, u