code: def test_gaussian_mixture_distribution(batch_inputs, event_inputs):
      num_samples = 250000
      sample_inputs = OrderedDict(particle=Bint[num_samples])
      be_inputs = OrderedDict(batch_inputs + event_inputs)
      int_inputs = OrderedDict(
          (k, d) for k, d in be_inputs.items() if d.dtype != "real"
      )
      batch_inputs = OrderedDict(batch_inputs)
      event_inputs = OrderedDict(event_inputs)
      sampled_vars = frozenset(["f"])
      p = random_gaussian(be_inputs) + 0.5 * random_tensor(int_inputs)
      p_marginal = p.reduce(ops.logaddexp, "e")
      assert isinstance(p_marginal, Tensor)
  
      rng_key = (
          None
          if get_backend() == "torch"
          else np.array([0, 1], dtype=np.uint32)
      )
      q = p.sample(sampled_vars, sample_inputs, rng_key=rng_key)
      q -= math.log(num_samples)
      q_marginal = q.reduce(ops.logaddexp, "e")
      q_marginal = p_marginal.materialize(q_marginal).reduce(
          ops.logaddexp, "particle"
      )
      assert isinstance(q_marginal, Tensor)
      q_marginal = q_marginal.align(tuple(p_marginal.inputs))
      assert_close(q_marginal, p_marginal, atol=0.15, rtol=None)
  
imports: import itertools
  import math
  from collections import OrderedDict
  from importlib import import_module
  import numpy
  import pytest
  import funsor.ops
  from funsor.cnf import Contraction
  from funsor.delta import Delta
  from funsor.distribution import BACKEND_TO_DISTRIBUTIONS_BACKEND
  from funsor.domains import Bint, Real, Reals
  from funsor.integrate import Integrate
  from funsor.montecarlo import MonteCarlo
  from funsor.tensor import Tensor, align_tensors
  from funsor.terms import Variable
  from funsor.testing import assert_close, excludes_backend, id_from_inputs, randn, random_gaussian, random_tensor, xfail_if_not_implemented
  from funsor.util import get_backend
label_desc: Use image node_id 7 for calling a global function with example usage: test_gaussian_mixture_distribution(batch_inputs, event_inputs) without return types