code: def test_saliency_shapes():
      """Test computing saliency maps for multiple outputs with multiple dimensions."""
  
      class SaliencyModel(torch.nn.Module):
          def __init__(self):
              super(SaliencyModel, self).__init__()
              self.layer1 = torch.nn.Linear(6, 4)
              self.layer2 = torch.nn.Linear(6, 5)
  
          def forward(self, x):
              x = torch.flatten(x)
              output1 = self.layer1(x).reshape(1, 4, 1)
              output2 = self.layer2(x).reshape(1, 1, 5)
              return output1, output2
  
      pytorch_model = SaliencyModel()
      model = dc.models.TorchModel(
          pytorch_model, dc.models.losses.L2Loss()
      )
      x = np.random.random((2, 3))
      s = model.compute_saliency(x)
      assert len(s) == 2
      assert s[0].shape == (4, 1, 2, 3)
      assert s[1].shape == (1, 5, 2, 3)
  
file_path: deepchem/deepchem/models/tests/test_torch_model.py
imports: import os
  import pytest
  import deepchem
  import numpy
  import math
  import unittest
label_desc: Use image node_id 9 for calling a global function with example usage: test_saliency_shapes() and returns: output1,  output2