code: def test_SACKernel(self):
      decomposition = {"1": [0, 3], "2": [1, 2]}
      kernel = SACKernel(
          decomposition=decomposition, batch_shape=torch.Size([])
      )
  
      self.assertIsInstance(kernel.kernel_dict, ModuleDict)
      self.assertIsInstance(kernel.base_kernel, MaternKernel)
      self.assertDictEqual(kernel.decomposition, decomposition)
  
      # test diag works well for lazy tensor
      x1 = torch.rand(5, 4)
      x2 = torch.rand(5, 4)
      res = kernel(x1, x2).to_dense()
      res_diag = kernel(x1, x2, diag=True)
      self.assertLess(torch.linalg.norm(res_diag - res.diag()), 1e-4)
  
      # test raise of ValueError
      with self.assertRaises(ValueError):
          SACKernel(
              decomposition={"1": [0, 3], "2": [1]},
              batch_shape=torch.Size([]),
          )
  
file_path: botorch/test/models/kernels/test_contextual.py
imports: import torch
  from botorch.models.kernels.contextual_lcea import get_order, get_permutation, is_contiguous, LCEAKernel
  from botorch.models.kernels.contextual_sac import SACKernel
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels.matern_kernel import MaternKernel
  from torch import Tensor
  from torch.nn import ModuleDict
label_desc: Use image node_id 1 for calling the ContextualKernelTest obj's underlying member method code with example usage: obj.test_SACKernel() without return types