code: def test_matching_layer_tuple_selector_fn(self) -> None:
      net = BasicModel_MultiLayer(multi_input_module=True)
      inp = torch.tensor([[0.0, 6.0, 0.0]])
  
      lc = LayerConductance(net, net.multi_relu)
      layer_attr = lc.attribute(
          inp, target=0, n_steps=500, method="gausslegendre"
      )
      nc = NeuronConductance(net, net.multi_relu)
      for i in range(len(layer_attr)):
          for j in range(layer_attr[i].shape[1]):
              neuron_attr = nc.attribute(
                  inp,
                  lambda x: x[i][:, j],
                  target=0,
                  n_steps=500,
                  method="gausslegendre",
              )
              self.assertAlmostEqual(
                  neuron_attr.sum().item(),
                  layer_attr[i][0][j].item(),
                  delta=0.005,
              )
  
imports: import unittest
  from typing import Any, Callable, cast, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.layer.layer_conductance import LayerConductance
  from captum.attr._core.neuron.neuron_conductance import NeuronConductance
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 14 for calling the Test obj's underlying member method code with example usage: obj.test_matching_layer_tuple_selector_fn() without return types