code: def test_basic_multilayer_wo_mult_by_inputs_agg_neurons(self) -> None:
      model = BasicModel_MultiLayer(inplace=True)
      model.eval()
  
      inputs = torch.tensor([[1.0, 20.0, 10.0]])
      baselines = torch.randn(2, 3)
  
      self._assert_attributions(
          model, model.linear1, inputs, baselines, (slice(0, 1, 1),), 60
      )
      self._assert_attributions(
          model,
          model.linear1,
          inputs,
          baselines,
          lambda x: x[:, 0:1],
          60,
      )
  
file_path: captum/tests/attr/neuron/test_neuron_gradient_shap.py
imports: from typing import Callable, Tuple, Union
  import torch
  from captum.attr._core.neuron.neuron_gradient_shap import NeuronGradientShap
  from captum.attr._core.neuron.neuron_integrated_gradients import NeuronIntegratedGradients
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_MultiLayer
  from tests.helpers.classification_models import SoftmaxModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 3 for calling the Test obj's underlying member method code with example usage: obj.test_basic_multilayer_wo_mult_by_inputs_agg_neurons() without return types