code: def test_nanmixture_gaussian_inference() -> None:
      nmdo = NanMixtureOutput(GaussianOutput())
  
      args_proj = nmdo.get_args_proj()
      args_proj.initialize()
      args_proj.hybridize()
  
      input = mx.nd.ones(NUM_SAMPLES)
  
      trainer = mx.gluon.Trainer(
          args_proj.collect_params(), "sgd", {"learning_rate": 0.00001}
      )
  
      mixture_samples = mx.nd.array(np_samples)
  
      N = 1000
      t = tqdm(list(range(N)))
      for _ in t:
          with mx.autograd.record():
              distr_args = args_proj(input)
              d = nmdo.distribution(distr_args)
              loss = d.loss(mixture_samples)
          loss.backward()
  
          loss_value = loss.mean().asnumpy()
          t.set_postfix({"loss": loss_value})
          trainer.step(NUM_SAMPLES)
  
      mu_hat = d.distribution.mu.asnumpy()
      sigma_hat = d.distribution.sigma.asnumpy()
      nan_prob_hat = d.nan_prob.asnumpy()
  
      assert (
          np.abs(mu - mu_hat) < TOL
      ), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
      assert (
          np.abs(sigma - sigma_hat) < TOL
      ), f"sigma did not match: sigma = {sigma}, sigma_hat = {sigma_hat}"
      assert (
          np.abs(nan_prob - nan_prob_hat) < TOL
      ), f"nan_prob did not match: nan_prob = {nan_prob}, nan_prob_hat = {nan_prob_hat}"
  
file_path: gluonts/test/mx/distribution/test_nan_mixture.py
imports: from typing import Dict
  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 Categorical, CategoricalOutput, Gaussian, GaussianOutput, NanMixture, NanMixtureOutput, StudentTOutput
label_desc: Use image node_id 3 for calling a global function with example usage: test_nanmixture_gaussian_inference() without return types