code: def test_deeplift_compare_with_and_without_inplace(self) -> None:
      model1 = ReLULinearModel(inplace=True)
      model2 = ReLULinearModel()
      x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True)
      x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True)
      inputs = (x1, x2)
      neuron_dl1 = NeuronDeepLift(model1, model1.relu)
      attributions1 = neuron_dl1.attribute(
          inputs, 0, attribute_to_neuron_input=False
      )
  
      neuron_dl2 = NeuronDeepLift(model2, model2.relu)
      attributions2 = neuron_dl2.attribute(
          inputs, 0, attribute_to_neuron_input=False
      )
  
      assertTensorAlmostEqual(self, attributions1[0], attributions2[0])
      assertTensorAlmostEqual(self, attributions1[1], attributions2[1])
  
imports: from __future__ import print_function
  from typing import Tuple, Union
  import torch
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.neuron.neuron_deep_lift import NeuronDeepLift, NeuronDeepLiftShap
  from tests.attr.layer.test_layer_deeplift import _create_inps_and_base_for_deeplift_neuron_layer_testing, _create_inps_and_base_for_deepliftshap_neuron_layer_testing
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_ConvNet_MaxPool3d, LinearMaxPoolLinearModel, ReLULinearModel
  from torch import Tensor
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.test_deeplift_compare_with_and_without_inplace() without return types