code: def _create_inps_and_base_for_deepliftshap_neuron_layer_testing() -> (
      Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor]]
  ):
      x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True)
      x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True)
  
      b1 = torch.tensor(
          [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
          requires_grad=True,
      )
      b2 = torch.tensor(
          [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
          requires_grad=True,
      )
  
      inputs = (x1, x2)
      baselines = (b1, b2)
  
      return inputs, baselines
  
imports: from __future__ import print_function
  from typing import cast, List, Tuple, Union
  import torch
  from captum.attr._core.layer.layer_deep_lift import LayerDeepLift, LayerDeepLiftShap
  from tests.helpers.basic import assert_delta, assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_ConvNet_MaxPool3d, BasicModel_MaxPool_ReLU, BasicModel_MultiLayer, LinearMaxPoolLinearModel, ReLULinearModel
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: _create_inps_and_base_for_deepliftshap_neuron_layer_testing() and returns: inputs,  baselines