code: def test_multi_forward_type(self):
      class AdaptiveTypeCheck(Typing):
          @property
          def input_types(self):
              if self.mode == "train":
                  return {"x": NeuralType(("B",), ElementType())}
  
              elif self.mode == "infer":
                  return {"y": NeuralType(("B",), ChannelType())}
  
              elif self.mode == "eval":
                  return {
                      "x": NeuralType(("B",), ElementType()),
                      "y": NeuralType(("B",), ChannelType()),
                  }
              else:
                  raise ValueError("Wrong mode of operation")
  
          @property
          def output_types(self):
              if self.mode == "train":
                  return {"u": NeuralType(("B",), ElementType())}
  
              elif self.mode == "infer":
                  return {"v": NeuralType(("B",), ChannelType())}
  
              elif self.mode == "eval":
                  return {
                      "u": NeuralType(("B",), ElementType()),
                      "v": NeuralType(("B",), ChannelType()),
                  }
              else:
                  raise ValueError("Wrong mode of operation")
  
          def __init__(self):
              self.mode = "train"
  
          def __call__(self, **kwargs):
              # Call should call and forward appropriate method in its own mode
              if self.mode == "train":
                  return self.train_forward(x=kwargs["x"])
  
              elif self.mode == "eval":
                  return self.eval_forward(x=kwargs["x"], y=kwargs["y"])
  
              elif self.mode == "infer":
                  return self.infer_forward(y=kwargs["y"])
  
          @typecheck()
          def train_forward(self, x):
              return x + 10
  
          @typecheck()
          def eval_forward(self, x, y):
              return x - 1, y - 1
  
          @typecheck()
          def infer_forward(self, y):
              return y - 10
  
          @property
          def mode(self):
              return self._mode
  
          @mode.setter
          def mode(self, val):
              if val not in ["train", "infer", "eval"]:
                  raise ValueError(
                      "mode must be either train infer or eval"
                  )
              self._mode = val
  
      obj = AdaptiveTypeCheck()
  
      x = torch.zeros(10)
      y = torch.full([10], fill_value=5, dtype=torch.int32)
  
      obj.mode = "train"
      x = obj(x=x)
  
      assert torch.all(x == 10)
      assert (
          x.neural_type.compare(NeuralType(("B",), ElementType()))
          == NeuralTypeComparisonResult.SAME
      )
  
      obj.mode = "eval"
      x, y = obj(x=x, y=y)
  
      assert torch.all(x == 9)
      assert torch.all(y == 4)
      assert (
          x.neural_type.compare(NeuralType(("B",), ElementType()))
          == NeuralTypeComparisonResult.SAME
      )
      assert (
          y.neural_type.compare(NeuralType(("B",), ChannelType()))
          == NeuralTypeComparisonResult.SAME
      )
  
      obj.mode = "infer"
      y = obj(y=y)
  
      assert torch.all(y == -6)
      assert (
          y.neural_type.compare(NeuralType(("B",), ChannelType()))
          == NeuralTypeComparisonResult.SAME
      )
  
      # Now perform assertions of wrong mode with wrong input combinations
      obj.mode = "train"
  
      # In train mode, call infer
      with pytest.raises(TypeError):
          _ = obj.eval_forward(x=x, y=y)
  
      with pytest.raises(TypeError):
          # wrong input + wrong mode
          _ = obj.infer_forward(y=x)
  
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 21 for calling the TestNeuralTypeCheckSystem obj's underlying member method code with example usage: obj.test_multi_forward_type() and returns: unknown, unknown,  unknown, unknown, self, dict, dict, self, dict, dict, self, dict, dict, self