code: def test_multitask_from_batch(self):
      mean = torch.randn(2, 3)
      variance = torch.randn(2, 3).clamp_min(1e-6)
      mvn = MultivariateNormal(mean, DiagLinearOperator(variance))
      mmvn = MultitaskMultivariateNormal.from_batch_mvn(
          mvn, task_dim=-1
      )
      self.assertTrue(isinstance(mmvn, MultitaskMultivariateNormal))
      self.assertEqual(mmvn.batch_shape, torch.Size([]))
      self.assertEqual(mmvn.event_shape, torch.Size([3, 2]))
      self.assertEqual(mmvn.covariance_matrix.shape, torch.Size([6, 6]))
      self.assertEqual(mmvn.mean, mean.transpose(-1, -2))
      self.assertEqual(mmvn.variance, variance.transpose(-1, -2))
  
      mean = torch.randn(2, 4, 3)
      variance = torch.randn(2, 4, 3).clamp_min(1e-6)
      mvn = MultivariateNormal(mean, DiagLinearOperator(variance))
      mmvn = MultitaskMultivariateNormal.from_batch_mvn(mvn, task_dim=0)
      self.assertTrue(isinstance(mmvn, MultitaskMultivariateNormal))
      self.assertEqual(mmvn.batch_shape, torch.Size([4]))
      self.assertEqual(mmvn.event_shape, torch.Size([3, 2]))
      self.assertEqual(
          mmvn.covariance_matrix.shape, torch.Size([4, 6, 6])
      )
      self.assertEqual(mmvn.mean, mean.permute(1, 2, 0))
      self.assertEqual(mmvn.variance, variance.permute(1, 2, 0))
  
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 17 for calling the TestMultiTaskMultivariateNormal obj's underlying member method code with example usage: obj.test_multitask_from_batch() without return types