code: def test_base_sample_shape(self):
      a = torch.randn(5, 10)
      lazy_square_a = RootLinearOperator(to_linear_operator(a))
      dist = MultivariateNormal(torch.zeros(5), lazy_square_a)
  
      # check that providing the base samples is okay
      samples = dist.rsample(
          torch.Size((16,)), base_samples=torch.randn(16, 10)
      )
      self.assertEqual(samples.shape, torch.Size((16, 5)))
  
      # check that an event shape of base samples fails
      self.assertRaises(
          RuntimeError,
          dist.rsample,
          torch.Size((16,)),
          base_samples=torch.randn(16, 5),
      )
  
      # check that the proper event shape of base samples is okay for
      # a non root lt
      nonlazy_square_a = to_linear_operator(lazy_square_a.to_dense())
      dist = MultivariateNormal(torch.zeros(5), nonlazy_square_a)
  
      samples = dist.rsample(
          torch.Size((16,)), base_samples=torch.randn(16, 5)
      )
      self.assertEqual(samples.shape, torch.Size((16, 5)))
  
imports: import math
  import unittest
  import torch
  from linear_operator import to_linear_operator
  from linear_operator.operators import DenseLinearOperator, DiagLinearOperator, LinearOperator, RootLinearOperator
  from torch.distributions import MultivariateNormal
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 18 for calling the TestMultivariateNormal obj's underlying member method code with example usage: obj.test_base_sample_shape() without return types