code: def _assert_attributions(
      self,
      model: Module,
      layer: Module,
      inputs: Tensor,
      baselines: Union[Tensor, Callable[..., Tensor]],
      neuron_ind: Union[int, Tuple[Union[int, slice], ...], Callable],
      n_samples: int = 5,
  ) -> None:
      ngs = NeuronGradientShap(model, layer)
      nig = NeuronIntegratedGradients(model, layer)
      attrs_gs = ngs.attribute(
          inputs,
          neuron_ind,
          baselines=baselines,
          n_samples=n_samples,
          stdevs=0.09,
      )
  
      if callable(baselines):
          baselines = baselines(inputs)
  
      attrs_ig = []
      for baseline in torch.unbind(baselines):
          attrs_ig.append(
              nig.attribute(
                  inputs, neuron_ind, baselines=baseline.unsqueeze(0)
              )
          )
      combined_attrs_ig = torch.stack(attrs_ig, dim=0).mean(dim=0)
      self.assertTrue(ngs.multiplies_by_inputs)
      assertTensorAlmostEqual(self, attrs_gs, combined_attrs_ig, 0.5)
  
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 5 for calling the Test obj's underlying member method code with example usage: obj._assert_attributions(model, layer, inputs, baselines, neuron_ind, n_samples) without return types