code: def _guided_backprop_matching_assert(
      self,
      model: Module,
      output_layer: Module,
      test_input: TensorOrTupleOfTensorsGeneric,
  ):
      out = model(test_input)
      attrib = GuidedBackprop(model)
      self.assertFalse(attrib.multiplies_by_inputs)
      neuron_attrib = NeuronGuidedBackprop(model, output_layer)
      for i in range(out.shape[1]):
          gbp_vals = attrib.attribute(test_input, target=i)
          neuron_gbp_vals = neuron_attrib.attribute(test_input, (i,))
          assertTensorAlmostEqual(
              self, gbp_vals, neuron_gbp_vals, delta=0.01
          )
  
file_path: captum/tests/attr/test_guided_backprop.py
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  from captum._utils.typing import TensorLikeList, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.guided_backprop_deconvnet import GuidedBackprop
  from captum.attr._core.neuron.neuron_guided_backprop_deconvnet import NeuronGuidedBackprop
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv
  from torch.nn import Module
label_desc: Use image node_id 9 for calling the Test obj's underlying member method code with example usage: obj._guided_backprop_matching_assert(model, output_layer, test_input) without return types