code: def test_matching_output_gradient(self) -> None:
      net = BasicModel_ConvNet()
      inp = 100 * torch.randn(2, 1, 10, 10, requires_grad=True)
      baseline = 20 * torch.randn(2, 1, 10, 10, requires_grad=True)
      self._ig_matching_test_assert(net, net.softmax, inp, baseline)
  
imports: import unittest
  from typing import Any, Callable, Tuple, Union
  import torch
  from captum._utils.typing import TensorLikeList, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.neuron.neuron_integrated_gradients import NeuronIntegratedGradients
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, 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 12 for calling the Test obj's underlying member method code with example usage: obj.test_matching_output_gradient() without return types