code: def test_multi_input_shapley_sampling_with_mask(self) -> None:
      net = BasicModel_MultiLayer_MultiInput()
      inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]])
      inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]])
      inp3 = torch.tensor([[0.0, 100.0, 10.0], [2.0, 10.0, 3.0]])
      mask1 = torch.tensor([[1, 1, 1], [0, 1, 0]])
      mask2 = torch.tensor([[0, 1, 2]])
      mask3 = torch.tensor([[0, 1, 2], [0, 0, 0]])
      expected = (
          [[1088.6666, 1088.6666, 1088.6666], [255.0, 595.0, 255.0]],
          [[76.6666, 1088.6666, 156.6666], [255.0, 595.0, 0.0]],
          [[76.6666, 1088.6666, 156.6666], [255.0, 255.0, 255.0]],
      )
      self._shapley_test_assert(
          net,
          (inp1, inp2, inp3),
          expected,
          additional_input=(1,),
          feature_mask=(mask1, mask2, mask3),
      )
      expected_with_baseline = (
          [[1040, 1040, 1040], [184, 580.0, 184]],
          [[52, 1040, 132], [184, 580.0, -12.0]],
          [[52, 1040, 132], [184, 184, 184]],
      )
      self._shapley_test_assert(
          net,
          (inp1, inp2, inp3),
          expected_with_baseline,
          additional_input=(1,),
          feature_mask=(mask1, mask2, mask3),
          baselines=(2, 3.0, 4),
          perturbations_per_eval=(1, 2, 3),
      )
  
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 9 for calling the Test obj's underlying member method code with example usage: obj.test_multi_input_shapley_sampling_with_mask() without return types