code: def test_mixture_inference() -> None:
      mdo = MixtureDistributionOutput(
          [GaussianOutput(), GaussianOutput()]
      )
  
      args_proj = mdo.get_args_proj()
      args_proj.initialize()
      args_proj.hybridize()
  
      input = mx.nd.ones((BATCH_SIZE, 1))
  
      distr_args = args_proj(input)
      d = mdo.distribution(distr_args)
  
      # plot_samples(d.sample())
  
      trainer = mx.gluon.Trainer(
          args_proj.collect_params(), "sgd", {"learning_rate": 0.02}
      )
  
      mixture_samples = mx.nd.array(np_samples)
  
      N = 1000
      t = tqdm(list(range(N)))
      for i in t:
          with mx.autograd.record():
              distr_args = args_proj(input)
              d = mdo.distribution(distr_args)
              loss = d.loss(mixture_samples)
          loss.backward()
          loss_value = loss.mean().asnumpy()
          t.set_postfix({"loss": loss_value})
          trainer.step(BATCH_SIZE)
  
      distr_args = args_proj(input)
      d = mdo.distribution(distr_args)
  
      obtained_hist = histogram(d.sample().asnumpy())
  
      # uncomment to see histograms
      # pl.plot(obtained_hist)
      # pl.plot(EXPECTED_HIST)
      # pl.show()
      assert diff(obtained_hist, EXPECTED_HIST) < 0.5
  
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 6 for calling a global function with example usage: test_mixture_inference() without return types