code: def _deeplift_assert(
      self,
      model: Module,
      attr_method: Union[DeepLift, DeepLiftShap],
      inputs: Tuple[Tensor, ...],
      baselines,
      custom_attr_func: Callable[..., Tuple[Tensor, ...]] = None,
  ) -> None:
      input_bsz = len(inputs[0])
      if callable(baselines):
          baseline_parameters = signature(baselines).parameters
          if len(baseline_parameters) > 0:
              baselines = baselines(inputs)
          else:
              baselines = baselines()
  
      baseline_bsz = (
          len(baselines[0])
          if isinstance(baselines[0], torch.Tensor)
          else 1
      )
      # Run attribution multiple times to make sure that it is
      # working as expected
      for _ in range(5):
          model.zero_grad()
          attributions, delta = attr_method.attribute(
              inputs,
              baselines,
              return_convergence_delta=True,
              custom_attribution_func=custom_attr_func,
          )
          attributions_without_delta = attr_method.attribute(
              inputs,
              baselines,
              custom_attribution_func=custom_attr_func,
          )
  
          for attribution, attribution_without_delta in zip(
              attributions, attributions_without_delta
          ):
              self.assertTrue(
                  torch.all(
                      torch.eq(attribution, attribution_without_delta)
                  )
              )
  
          if isinstance(attr_method, DeepLiftShap):
              self.assertEqual(
                  [input_bsz * baseline_bsz], list(delta.shape)
              )
          else:
              self.assertEqual([input_bsz], list(delta.shape))
              delta_external = attr_method.compute_convergence_delta(
                  attributions, baselines, inputs
              )
              assertTensorAlmostEqual(
                  self, delta, delta_external, delta=0.0, mode="max"
              )
  
          delta_condition = (delta.abs() < 0.00001).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),
          )
          for input, attribution in zip(inputs, attributions):
              self.assertEqual(input.shape, attribution.shape)
          if (
              isinstance(baselines[0], (int, float))
              or inputs[0].shape == baselines[0].shape
          ):
              # Compare with Integrated Gradients
              ig = IntegratedGradients(model)
              attributions_ig = ig.attribute(inputs, baselines)
              assertAttributionComparision(
                  self, attributions, attributions_ig
              )
  
imports: from inspect import signature
  from typing import Callable, List, Tuple, Union
  import torch
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from tests.helpers.basic import assertAttributionComparision, assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModelWithReusedModules, Conv1dSeqModel, LinearMaxPoolLinearModel, ReLUDeepLiftModel, ReLULinearModel, TanhDeepLiftModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 19 for calling the Test obj's underlying member method code with example usage: obj._deeplift_assert(model, attr_method, inputs, baselines, custom_attr_func) without return types