code: def test_mixture(
      distr1: Distribution,
      distr2: Distribution,
      p: Tensor,
      serialize_fn,
  ) -> None:
      # sample from component distributions, and select samples
      samples1 = distr1.sample(num_samples=NUM_SAMPLES_LARGE)
      samples2 = distr2.sample(num_samples=NUM_SAMPLES_LARGE)
  
      # TODO: for multivariate case, test should not sample elements from different components in the event_dim dimension
      rand = mx.nd.random.uniform(shape=(NUM_SAMPLES_LARGE, *p.shape))
      choice = (rand < p.expand_dims(axis=0)).broadcast_like(samples1)
      samples_ref = mx.nd.where(choice, samples1, samples2)
  
      # construct mixture distribution and sample from it
  
      mixture_probs = mx.nd.stack(p, 1.0 - p, axis=-1)
  
      mixture = MixtureDistribution(
          mixture_probs=mixture_probs, components=[distr1, distr2]
      )
      mixture = serialize_fn(mixture)
  
      samples_mix = mixture.sample(num_samples=NUM_SAMPLES_LARGE)
  
      # check that shapes are right
  
      assert (
          samples1.shape
          == samples2.shape
          == samples_mix.shape
          == samples_ref.shape
      )
  
      # check mean and stddev
      calc_mean = mixture.mean.asnumpy()
      calc_std = mixture.stddev.asnumpy()
      sample_mean = samples_mix.asnumpy().mean(axis=0)
      sample_std = samples_mix.asnumpy().std(axis=0)
  
      assert np.allclose(calc_mean, sample_mean, atol=1e-1)
      assert np.allclose(calc_std, sample_std, atol=2e-1)
  
      # check that histograms are close
      assert (
          diff(
              histogram(samples_mix.asnumpy()),
              histogram(samples_ref.asnumpy()),
          )
          < 0.05
      )
  
      # can only calculated cdf for gaussians currently
      if isinstance(distr1, Gaussian) and isinstance(distr2, Gaussian):
          emp_cdf, edges = empirical_cdf(samples_mix.asnumpy())
          calc_cdf = mixture.cdf(mx.nd.array(edges)).asnumpy()
          assert np.allclose(calc_cdf[1:, :], emp_cdf, atol=1e-2)
  
file_path: gluonts/test/mx/distribution/test_mixture.py
imports: import mxnet
  import numpy
  import pytest
  from gluonts.core.serde import dump_json, load_json
  from gluonts.gluonts_tqdm import tqdm
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import Gamma, GammaOutput, Gaussian, GaussianOutput, GenPareto, GenParetoOutput, LaplaceOutput, MixtureDistribution, MixtureDistributionOutput, MultivariateGaussianOutput, StudentT, StudentTOutput
  from gluonts.mx.distribution.distribution import Distribution
  from gluonts.mx.distribution.distribution_output import DistributionOutput
  from gluonts.testutil import empirical_cdf
label_desc: Use image node_id 4 for calling a global function with example usage: test_mixture(distr1, distr2, p, serialize_fn) without return types