code: def test_multi_input_shapley_sampling_without_mask(self) -> None:
      net = BasicModel_MultiLayer_MultiInput()
      inp1 = torch.tensor([[23.0, 0.0, 0.0], [20.0, 50.0, 30.0]])
      inp2 = torch.tensor([[20.0, 0.0, 50.0], [0.0, 100.0, 0.0]])
      inp3 = torch.tensor([[0.0, 100.0, 10.0], [0.0, 10.0, 0.0]])
      expected = (
          [[90, 0, 0], [78.0, 198.0, 118.0]],
          [[78, 0, 198], [0.0, 398.0, 0.0]],
          [[0, 398, 38], [0.0, 38.0, 0.0]],
      )
      self._shapley_test_assert(
          net,
          (inp1, inp2, inp3),
          expected,
          additional_input=(1,),
          n_samples=200,
          test_true_shapley=False,
      )
  
imports: import io
  import unittest
  import unittest.mock
  from typing import Any, Callable, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.shapley_value import ShapleyValues, ShapleyValueSampling
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput, BasicModelBoolInput
label_desc: Use image node_id 8 for calling the Test obj's underlying member method code with example usage: obj.test_multi_input_shapley_sampling_without_mask() without return types