code: def test_nested_mixed_input_output_neural_types(self):
      class NestedMixedNodeA(Typing):
          @property
          def input_types(self):
              return {
                  "x1": NeuralType(("B",), ElementType()),
                  "x2": [[NeuralType(("B",), ElementType())]],
              }
  
          @property
          def output_types(self):
              return {
                  "y0": NeuralType(("B", "D"), LogprobsType()),
                  "y1": [[NeuralType(("B", "D"), LogitsType())]],
              }
  
          @typecheck(ignore_collections=False)
          def __call__(self, x1, x2):
              # input x = [[x1, x2], [x3]]
              x0 = x2[0][0]
              y = torch.randn(x0.shape[0], 4)
              return y, [[y, y], [y]]
  
      # Non-homogeneous output types
      class NestedMixedNodeB(Typing):
          @property
          def input_types(self):
              return {"w": [[NeuralType(("B", "D"), LogitsType())]]}
  
          @property
          def output_types(self):
              return {
                  "u0": [
                      NeuralType(("B",), LogprobsType())
                  ],  # check non homogeneous type
                  "u1": NeuralType(("B",), LabelsType()),
              }
  
          @typecheck(ignore_collections=False)
          def __call__(self, w):
              # input x = [[x1, x2], [x3]]
              _, u00 = w[0][0].max(-1)
              _, u01 = w[0][1].max(-1)
              _, u10 = w[1][0].max(-1)
              return [u00, u01], u10
  
      nodeA = NestedMixedNodeA()
      nodeB = NestedMixedNodeB()
  
      input_nest = [
          [torch.zeros(10), torch.zeros(10)],
          [torch.zeros(10)],
      ]
      out_y, outA = nodeA(x1=torch.zeros(10), x2=input_nest)
      outB, out_u = nodeB(w=outA)
  
      # Perform recursive shape assert
      assert out_y.neural_type.compare(
          NeuralType(("B", "D"), LogprobsType())
      )
      recursive_assert_shape(outB, torch.Size([10]))
  
      # Assert non-homogeneous type assertions
      assert (
          outB[0].neural_type.compare(
              NeuralType(("B",), LogprobsType())
          )
          == NeuralTypeComparisonResult.SAME
      )
      assert (
          outB[1].neural_type.compare(
              NeuralType(("B",), LogprobsType())
          )
          == NeuralTypeComparisonResult.SAME
      )
      assert (
          out_u.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 20 for calling the TestNeuralTypeCheckSystem obj's underlying member method code with example usage: obj.test_nested_mixed_input_output_neural_types() and returns: dict, dict, y,  list, dict, dict, list,  u10