code: def test_rsample_distribution(batch_shape, dim):
      num_samples = 20000
      mvn = random_mvn(batch_shape, dim)
      g = mvn_to_gaussian(mvn)
      expected = mvn.rsample((num_samples,))
      actual = g.rsample((num_samples,))
  
      def get_moments(x):
          mean = x.mean(0)
          x = x - mean
          cov = (x.unsqueeze(-1) * x.unsqueeze(-2)).mean(0)
          std = cov.diagonal(dim1=-1, dim2=-2).sqrt()
          corr = cov / (std.unsqueeze(-1) * std.unsqueeze(-2))
          return mean, std, corr
  
      expected_mean, expected_std, expected_corr = get_moments(expected)
      actual_mean, actual_std, actual_corr = get_moments(actual)
      assert_close(actual_mean, expected_mean, atol=0.1, rtol=0.02)
      assert_close(actual_std, expected_std, atol=0.1, rtol=0.02)
      assert_close(actual_corr, expected_corr, atol=0.05)
  
imports: import math
  from collections import OrderedDict
  import pytest
  import torch
  from torch.distributions import constraints, transform_to
  from torch.nn.functional import pad
  import pyro.distributions
  from pyro.distributions.util import broadcast_shape
  from pyro.ops.gaussian import AffineNormal, Gaussian, gaussian_tensordot, matrix_and_gaussian_to_gaussian, matrix_and_mvn_to_gaussian, mvn_to_gaussian, sequential_gaussian_filter_sample, sequential_gaussian_tensordot
  from tests.common import assert_close
  from tests.ops.gaussian import assert_close_gaussian, random_gaussian, random_mvn
label_desc: Use image node_id 7 for calling a global function with example usage: test_rsample_distribution(batch_shape, dim) and returns: mean,  std,  corr