code: def test_multi_sample_ablation_with_selector_fn(self) -> None:
      net = BasicModel_MultiLayer()
      inp = torch.tensor(
          [[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]], requires_grad=True
      )
      mask = torch.tensor([[0, 0, 1], [1, 1, 0]])
      self._ablation_test_assert(
          net,
          net.linear2,
          inp,
          [[82.0, 82.0, 24.0], [560.0, 560.0, 240.0]],
          feature_mask=mask,
          perturbations_per_eval=(1, 2, 3),
          neuron_selector=lambda x: torch.sum(x, dim=1),
      )
  
file_path: captum/tests/attr/neuron/test_neuron_ablation.py
imports: import unittest
  from typing import Any, Callable, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType, TensorLikeList, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.neuron.neuron_feature_ablation import NeuronFeatureAblation
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  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_multi_sample_ablation_with_selector_fn() without return types