code: def create_random_gmm(n_mix, n_features, covariance_type, prng=0):
      prng = check_random_state(prng)
      g = GaussianMixture(n_mix, covariance_type=covariance_type)
      g.means_ = prng.randint(-20, 20, (n_mix, n_features))
      g.covars_ = make_covar_matrix(covariance_type, n_mix, n_features)
      g.weights_ = normalized(prng.rand(n_mix))
      return g
  
imports: import numpy
  from numpy.testing import assert_allclose
  import pytest
  from sklearn.mixture import GaussianMixture
  from sklearn.utils import check_random_state
  from hmmlearn import hmm
  from .None import assert_log_likelihood_increasing, make_covar_matrix, normalized
label_desc: Use image node_id 1 for calling a global function with example usage: create_random_gmm(n_mix, n_features, covariance_type, prng) and returns: g