code: def test_simple_gmm():
      x = np.array([1.0, 1.0, 2.0, 3.0, 1.0])
      d = gmm_1d_distribution(x, min_limit=0.0, max_limit=4.0)
      assert d(1.1) > d(3.5), "GMM distribution not behaving correctly"
      assert d(2.0) > d(3.0), "GMM distribution not behaving correctly"
      assert d(-1.0) == 0, "GMM distribution out of bounds error"
      assert d(9.0) == 0, "GMM distribution out of bounds error"
  
      samples = d.get_samples(n=25)
      np.testing.assert_array_less(samples, 4.0)
      np.testing.assert_array_less(0.0, samples)
  
imports: import pytest
  import numpy
  import scipy.stats
  from brainiak.hyperparamopt.hpo import gmm_1d_distribution, fmin
label_desc: Use image node_id 1 for calling a global function with example usage: test_simple_gmm() without return types