code: def test_simple_ig_multi_input_relu_batch(self) -> None:
      net = BasicModel_MultiLayer_MultiInput()
      inp1 = torch.tensor([[0.0, 6.0, 14.0], [0.0, 80.0, 0.0]])
      inp2 = torch.tensor([[0.0, 6.0, 14.0], [0.0, 20.0, 0.0]])
      inp3 = torch.tensor([[0.0, 0.0, 0.0], [0.0, 20.0, 0.0]])
      self._ig_input_test_assert(
          net,
          net.model.relu,
          (inp1, inp2),
          (0,),
          (
              [[0.0, 1.5, 3.5], [0.0, 40.0, 0.0]],
              [[0.0, 1.5, 3.5], [0.0, 10.0, 0.0]],
          ),
          (inp3, 0.5),
      )
  
imports: import unittest
  from typing import Any, Callable, Tuple, Union
  import torch
  from captum._utils.typing import TensorLikeList, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.neuron.neuron_integrated_gradients import NeuronIntegratedGradients
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 10 for calling the Test obj's underlying member method code with example usage: obj.test_simple_ig_multi_input_relu_batch() without return types