docstr: Ensure slicing is efficient when using a multioutput kernel
docstr_tok: ["Ensure","slicing","is","efficient","when","using","a","multioutput","kernel"]
code: def test_batch_getitem_multioutput(self):
      """Ensure slicing is efficient when using a multioutput kernel"""
      x1 = torch.randn(5, 6)
      x2 = torch.randn(5, 6)
      kern = gpytorch.kernels.RBFKernelGrad(batch_shape=torch.Size([2]))
      k = kern(x1, x2)
      k.evaluate_kernel = MagicMock(name="evaluate_kernel")
      k_sliced = k[..., :7, :14]
      self.assertFalse(k.evaluate_kernel.called)
      self.assertEqual(k.size(), torch.Size([2, 35, 35]))
      self.assertEqual(k_sliced.size(), torch.Size([2, 7, 14]))
  
imports: import math
  import unittest
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  from linear_operator import to_dense
  from linear_operator.test.linear_operator_test_case import LinearOperatorTestCase
  import gpytorch
label_desc: Use image node_id 7 for calling the TestLazyEvaluatedKernelTensorBatch obj's underlying member method code with example usage: obj.test_batch_getitem_multioutput() without return types