code: def test_marginalize_condition(
      sample_shape, batch_shape, left, right
  ):
      dim = left + right
      g = random_gamma_gaussian(batch_shape, dim)
      x = torch.randn(sample_shape + (1,) * len(batch_shape) + (right,))
      s = torch.randn(batch_shape).exp()
      assert_close(
          g.marginalize(left=left).log_density(x, s),
          g.condition(x).event_logsumexp().log_density(s),
      )
  
imports: import math
  import pytest
  import torch
  from torch.nn.functional import pad
  import pyro.distributions
  from pyro.distributions.util import broadcast_shape
  from pyro.ops.gamma_gaussian import GammaGaussian, gamma_and_mvn_to_gamma_gaussian, gamma_gaussian_tensordot, matrix_and_mvn_to_gamma_gaussian
  from tests.common import assert_close
  from tests.ops.gamma_gaussian import assert_close_gamma_gaussian, random_gamma, random_gamma_gaussian
  from tests.ops.gaussian import random_mvn
label_desc: Use image node_id 8 for calling a global function with example usage: test_marginalize_condition(sample_shape, batch_shape, left, right) without return types