code: def test_getitem(self):
      shape = (2, 4, 3, 2)
      cov = torch.randn(*shape, shape[1])
      cov = cov @ cov.transpose(-1, -2)
      mean = torch.randn(*shape)
      dist = MultivariateNormal(mean, cov)
      dist_cov = dist.covariance_matrix
  
      d = dist[1]
      assert torch.equal(d.mean, dist.mean[1])
      self.assertAllClose(d.covariance_matrix, dist_cov[1])
  
      d = dist[..., 1]
      assert torch.equal(d.mean, dist.mean[..., 1])
      cov = dist_cov[..., 1, 1]
      self.assertAllClose(
          d.covariance_matrix, cov.unsqueeze(-1) * torch.eye(shape[-2])
      )
  
      d = dist[:, [2, 3], :, 1:]
      assert torch.equal(d.mean, dist.mean[:, [2, 3], :, 1:])
      self.assertAllClose(
          d.covariance_matrix, dist_cov[:, [2, 3], :, 1:, 1:]
      )
  
      d = dist[:, :, ..., [0, 1, 1, 0]]
      assert torch.equal(d.mean, dist.mean[..., [0, 1, 1, 0]])
      self.assertAllClose(
          d.covariance_matrix,
          dist_cov[..., [0, 1, 1, 0], :][..., [0, 1, 1, 0]],
      )
  
      d = dist[1, 2, 2, ...]
      assert torch.equal(d.mean, dist.mean[1, 2, 2, :])
      self.assertAllClose(d.covariance_matrix, dist_cov[1, 2, 2, :, :])
  
      d = dist[0, 1, ..., 2, 1]
      assert torch.equal(d.mean, dist.mean[0, 1, 2, 1])
      self.assertAllClose(d.covariance_matrix, dist_cov[0, 1, 2, 1, 1])
  
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 17 for calling the TestMultivariateNormal obj's underlying member method code with example usage: obj.test_getitem() without return types