code: def test_compare_with_emb_patching(self) -> None:
      input1 = torch.tensor([[2, 5, 0, 1]])
      baseline1 = torch.tensor([[0, 0, 0, 0]])
      # these ones will be use as an additional forward args
      input2 = torch.tensor([[0, 2, 4, 1]])
      input3 = torch.tensor([[2, 3, 0, 1]])
  
      self._assert_compare_with_emb_patching(
          input1, baseline1, additional_args=(input2, input3)
      )
  
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 1 for calling the Test obj's underlying member method code with example usage: obj.test_compare_with_emb_patching() without return types