code: def test_condition(sample_shape, batch_shape, left, right):
      dim = left + right
      g = random_gamma_gaussian(batch_shape, dim)
      g.precision += torch.eye(dim) * 0.1
      value = torch.randn(
          sample_shape + (1,) * len(batch_shape) + (dim,)
      )
      left_value, right_value = value[..., :left], value[..., left:]
  
      conditioned = g.condition(right_value)
      assert conditioned.batch_shape == sample_shape + g.batch_shape
      assert conditioned.dim() == left
  
      s = torch.randn(batch_shape).exp()
      actual = conditioned.log_density(left_value, s)
      expected = g.log_density(value, s)
      assert_close(actual, expected)
  
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 9 for calling a global function with example usage: test_condition(sample_shape, batch_shape, left, right) without return types