code: def _assert_attributions(
      self,
      model: Module,
      attributions: Tensor,
      inputs: Tensor,
      baselines: Union[Tensor, int, float],
      delta: Tensor,
      target: TargetType = None,
  ) -> None:
      self.assertEqual(inputs.shape, attributions.shape)
  
      delta_condition = (delta.abs() < 0.003).all()
      self.assertTrue(
          delta_condition,
          "The sum of attribution values {} is not "
          "nearly equal to the difference between the endpoint for "
          "some samples".format(delta),
      )
      # compare with integrated gradients
      if (
          isinstance(baselines, (int, float))
          or inputs.shape == baselines.shape
      ):
          ig = IntegratedGradients(model)
          attributions_ig = ig.attribute(
              inputs, baselines=baselines, target=target
          )
          assertAttributionComparision(
              self, attributions, attributions_ig
          )
  
imports: from typing import Union
  import torch
  from captum._utils.typing import TargetType
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from tests.helpers.basic import assertAttributionComparision, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_ConvNet_MaxPool1d, BasicModel_ConvNet_MaxPool3d
  from tests.helpers.classification_models import SigmoidDeepLiftModel, SoftmaxDeepLiftModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 14 for calling the Test obj's underlying member method code with example usage: obj._assert_attributions(model, attributions, inputs, baselines, delta, target) without return types