code: def test_multi_sample_shapley_sampling_with_mask(self) -> None:
      net = BasicModel_MultiLayer()
      inp = torch.tensor(
          [[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]], requires_grad=True
      )
      mask = torch.tensor([[0, 0, 1], [1, 1, 0]])
      self._shapley_test_assert(
          net,
          inp,
          [[39.5, 39.5, 10.5], [275.0, 275.0, 115.0]],
          feature_mask=mask,
          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 7 for calling the Test obj's underlying member method code with example usage: obj.test_multi_sample_shapley_sampling_with_mask() without return types