code: def _assert_compare_with_layer_conductance(
      self,
      model: Module,
      input: Tensor,
      attribute_to_layer_input: bool = False,
  ):
      lc = LayerConductance(model, cast(Module, model.linear2))
      # For large number of steps layer conductance and layer integrated gradients
      # become very close
      attribution, delta = lc.attribute(
          input,
          target=0,
          n_steps=1500,
          return_convergence_delta=True,
          attribute_to_layer_input=attribute_to_layer_input,
      )
      lig = LayerIntegratedGradients(model, cast(Module, model.linear2))
      attributions2, delta2 = lig.attribute(
          input,
          target=0,
          n_steps=1500,
          return_convergence_delta=True,
          attribute_to_layer_input=attribute_to_layer_input,
      )
      assertTensorAlmostEqual(
          self, attribution, attributions2, delta=0.01, mode="max"
      )
      assertTensorAlmostEqual(
          self, delta, delta2, delta=0.5, mode="max"
      )
  
imports: from typing import Any, cast, List, Tuple, Union
  import torch
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.layer.layer_activation import LayerActivation
  from captum.attr._core.layer.layer_conductance import LayerConductance
  from captum.attr._core.layer.layer_integrated_gradients import LayerIntegratedGradients
  from captum.attr._models.base import configure_interpretable_embedding_layer, remove_interpretable_embedding_layer
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicEmbeddingModel, BasicModel_MultiLayer, BasicModel_MultiLayer_TrueMultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 11 for calling the Test obj's underlying member method code with example usage: obj._assert_compare_with_layer_conductance(model, input, attribute_to_layer_input) without return types