code: def test_getitem_interleaved(self):
      mean_shape = (2, 4, 3, 2)
      covar_shape = (2, 4, 6, 6)
      mean = torch.randn(mean_shape)
      _covar = torch.randn(covar_shape)
      covar = _covar @ _covar.transpose(-1, -2)
      distribution = MultitaskMultivariateNormal(
          mean, covar, validate_args=True
      )
  
      def flat(observation: int, task: int) -> int:
          return observation * 2 + task
  
      part = distribution[1, -1]
      self.assertIsInstance(part, MultitaskMultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size(()))
      self.assertEqual(part.event_shape, torch.Size((3, 2)))
      self.assertAllClose(part.mean, mean[1, -1])
      self.assertAllClose(part.covariance_matrix, covar[1, -1])
  
      part = distribution[1, 0, ...]
      self.assertIsInstance(part, MultitaskMultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size(()))
      self.assertEqual(part.event_shape, torch.Size((3, 2)))
      self.assertAllClose(part.mean, mean[1, 0])
      self.assertAllClose(part.covariance_matrix, covar[1, 0])
  
      part = distribution[..., 2, 1]
      self.assertFalse(isinstance(part, MultitaskMultivariateNormal))
      self.assertIsInstance(part, MultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((2,)))
      self.assertEqual(part.event_shape, (4,))
      self.assertAllClose(part.mean, mean[..., 2, 1])
      self.assertAllClose(
          part.covariance_matrix,
          torch.diag_embed(covar[:, :, flat(2, 1), flat(2, 1)]),
      )
  
      part = distribution[1, ..., -2]
      self.assertFalse(isinstance(part, MultitaskMultivariateNormal))
      self.assertIsInstance(part, MultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((4,)))
      self.assertEqual(part.event_shape, torch.Size((3,)))
      self.assertAllClose(part.mean, mean[1, :, :, 0])
      self.assertAllClose(part.covariance_matrix, covar[1, :, ::2, ::2])
  
      part = distribution[..., 2, :]
      self.assertFalse(isinstance(part, MultitaskMultivariateNormal))
      self.assertIsInstance(part, MultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((2, 4)))
      self.assertEqual(part.event_shape, torch.Size((2,)))
      self.assertAllClose(part.mean, mean[:, :, 2, :])
      self.assertAllClose(
          part.covariance_matrix,
          covar[:, :, 2 * 2 : 3 * 2, 2 * 2 : 3 * 2],
      )
  
      part = distribution[0, :, :, torch.tensor([1, 0])]
      self.assertIsInstance(part, MultitaskMultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((4,)))
      self.assertEqual(part.event_shape, torch.Size((3, 2)))
      self.assertAllClose(part.mean, mean[0, ..., torch.tensor([1, 0])])
      indices = torch.tensor([1, 0, 3, 2, 5, 4])
      self.assertAllClose(
          part.covariance_matrix, covar[0, :, indices][..., indices]
      )
  
      part = distribution[:, 1, torch.tensor([2, 0])]
      self.assertIsInstance(part, MultitaskMultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((2,)))
      self.assertEqual(part.event_shape, torch.Size((2, 2)))
      self.assertAllClose(part.mean, mean[:, 1, torch.tensor([2, 0])])
      indices = torch.tensor([4, 5, 0, 1])
      self.assertAllClose(
          part.covariance_matrix, covar[:, 1, indices][..., indices]
      )
  
      part = distribution[..., 1:, :-1]
      self.assertIsInstance(part, MultitaskMultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((2, 4)))
      self.assertEqual(part.event_shape, torch.Size((2, 1)))
      self.assertAllClose(part.mean, mean[..., 1:, :-1])
      indices = torch.tensor([flat(1, 0), flat(2, 0)])
      self.assertAllClose(
          part.covariance_matrix, covar[..., indices, :][..., indices]
      )
  
      part = distribution[
          ..., torch.tensor([2, 0, 2]), torch.tensor([1, 0, 0])
      ]
      self.assertFalse(isinstance(part, MultitaskMultivariateNormal))
      self.assertIsInstance(part, MultivariateNormal)
      self.assertEqual(part.batch_shape, torch.Size((2, 4)))
      self.assertEqual(part.event_shape, torch.Size((3,)))
      self.assertAllClose(
          part.mean,
          mean[..., torch.tensor([2, 0, 2]), torch.tensor([1, 0, 0])],
      )
      indices = torch.tensor([flat(2, 1), flat(0, 0), flat(2, 0)])
      self.assertAllClose(
          part.covariance_matrix, covar[..., indices, :][..., indices]
      )
  
imports: import math
  import os
  import random
  import unittest
  import torch
  from linear_operator.operators import DiagLinearOperator, KroneckerProductLinearOperator
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 22 for calling the TestMultiTaskMultivariateNormal obj's underlying member method code with example usage: obj.test_getitem_interleaved() and returns: unknown