code: def test_simple_multi_input_conv(self) -> None:
      net = BasicModel_ConvNet_One_Conv()
      inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
      inp2 = torch.ones((1, 1, 4, 4))
      self._ablation_test_assert(
          net,
          net.relu2,
          (inp, inp2),
          (67 * torch.ones_like(inp), 13 * torch.ones_like(inp2)),
          feature_mask=(torch.tensor(0), torch.tensor(1)),
          perturbations_per_eval=(1, 2, 4, 8, 12, 16),
      )
      self._ablation_test_assert(
          net,
          net.relu2,
          (inp, inp2),
          (
              [
                  [
                      [
                          [0.0, 2.0, 4.0, 3.0],
                          [4.0, 9.0, 10.0, 7.0],
                          [4.0, 13.0, 14.0, 11.0],
                          [0.0, 0.0, 0.0, 0.0],
                      ]
                  ]
              ],
              [
                  [
                      [
                          [1.0, 2.0, 2.0, 1.0],
                          [1.0, 2.0, 2.0, 1.0],
                          [1.0, 2.0, 2.0, 1.0],
                          [0.0, 0.0, 0.0, 0.0],
                      ]
                  ]
              ],
          ),
          perturbations_per_eval=(1, 3, 7, 14),
      )
  
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 7 for calling the Test obj's underlying member method code with example usage: obj.test_simple_multi_input_conv() without return types