code: def test_multi_sample_shapley_sampling(self) -> None:
      net = BasicModel_MultiLayer()
      inp = torch.tensor([[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]])
      self._shapley_test_assert(
          net,
          inp,
          [[7.0, 32.5, 10.5], [76.66666, 196.66666, 116.66666]],
          perturbations_per_eval=(1, 2, 3),
          n_samples=200,
      )
  
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 6 for calling the Test obj's underlying member method code with example usage: obj.test_multi_sample_shapley_sampling() without return types