code: def test_convnet_with_maxpool1d_large_baselines(self) -> None:
      input = 100 * torch.randn(2, 1, 10, requires_grad=True)
      baseline = 500 * torch.randn(2, 1, 10)
  
      model = BasicModel_ConvNet_MaxPool1d()
      dl = DeepLift(model)
  
      self.softmax_classification(
          model, dl, input, baseline, torch.tensor(2)
      )
  
imports: from typing import Union
  import torch
  from captum._utils.typing import TargetType
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from tests.helpers.basic import assertAttributionComparision, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_ConvNet_MaxPool1d, BasicModel_ConvNet_MaxPool3d
  from tests.helpers.classification_models import SigmoidDeepLiftModel, SoftmaxDeepLiftModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 12 for calling the Test obj's underlying member method code with example usage: obj.test_convnet_with_maxpool1d_large_baselines() without return types