code: def test_to_data_independent_dist(
      self, dtype=torch.float, device="cpu", interleaved=True
  ):
      # Create a fake covariance
      factor = torch.randn(4, 4, device=device, dtype=dtype)
      data_covar = factor.mT @ factor
      task_covar = torch.tensor(
          [[1.0, 0.3, 0.1], [0.3, 1.0, 0.3], [0.1, 0.3, 1.0]],
          device=device,
          dtype=dtype,
      )
      if interleaved:
          covar = KroneckerProductLinearOperator(data_covar, task_covar)
      else:
          covar = KroneckerProductLinearOperator(task_covar, data_covar)
  
      mean = torch.randn(4, 3, device=device, dtype=dtype)
      dist = MultitaskMultivariateNormal(
          mean, covar, interleaved=interleaved
      )
  
      res = dist.to_data_independent_dist(jitter_val=1e-4)
      self.assertEqual(res.mean, mean)
      data_var = data_covar.diagonal(dim1=-1, dim2=-2)
      jitter = torch.eye(3, dtype=dtype, device=device) * 1e-4
      self.assertAllClose(
          res.covariance_matrix,
          data_var.view(-1, 1, 1) * task_covar + jitter,
      )
  
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 15 for calling the TestMultiTaskMultivariateNormal obj's underlying member method code with example usage: obj.test_to_data_independent_dist(dtype, device, interleaved) without return types