code: def test_basic_multilayer_wo_multiplying_by_inputs(self) -> None:
      model = BasicModel_MultiLayer(inplace=True)
      model.eval()
  
      inputs = torch.tensor([[1.0, -20.0, 10.0]])
      baselines = torch.zeros(3, 3)
      lgs = LayerGradientShap(
          model, model.linear2, multiply_by_inputs=False
      )
      attrs = lgs.attribute(
          inputs,
          baselines,
          target=0,
          stdevs=0.0,
      )
      assertTensorAlmostEqual(self, attrs, torch.tensor([[1.0, 0.0]]))
  
imports: from typing import Any, Callable, List, Tuple, Union
  import torch
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.gradient_shap import GradientShap
  from captum.attr._core.layer.layer_gradient_shap import LayerGradientShap
  from tests.attr.test_gradient_shap import _assert_attribution_delta
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from tests.helpers.classification_models import SoftmaxModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.test_basic_multilayer_wo_multiplying_by_inputs() without return types