code: def test_inverse_quantile() -> None:
      r"""
      Tests CDF(Q(x)) == x.
      """
      mu = mx.nd.array([1])
      sigma = mx.nd.array([0.1])
      uni_bound = mx.nd.array([1])
      mixture_probs = mx.nd.array([[0.3, 0.7]])
  
      levels = mx.nd.array([0.01, 0.1, 0.5, 0.9, 0.99])
  
      mix_gau = MixtureDistribution(
          mixture_probs=mixture_probs,
          components=[Gaussian(mu, sigma), Gaussian(mu, sigma)],
      )
      mix_uni = MixtureDistribution(
          mixture_probs=mixture_probs,
          components=[
              Uniform(uni_bound, 3 * uni_bound),
              Uniform(uni_bound, 3 * uni_bound),
          ],
      )
      mix_gau_uni = MixtureDistribution(
          mixture_probs=mixture_probs,
          components=[
              Gaussian(mu, sigma),
              Uniform(uni_bound, 3 * uni_bound),
          ],
      )
  
      gau_res = mix_gau.cdf(mix_gau.quantile(levels))
      uni_res = mix_uni.cdf(mix_uni.quantile(levels))
      gau_uni_res = mix_gau_uni.cdf(mix_gau_uni.quantile(levels))
  
      assert (
          np.max(
              gau_res.asnumpy() / levels.asnumpy().reshape(-1, 1) - 1
          )
          < 1e-4
      )
      assert (
          np.max(
              uni_res.asnumpy() / levels.asnumpy().reshape(-1, 1) - 1
          )
          < 1e-4
      )
      assert (
          np.max(
              gau_uni_res.asnumpy() / levels.asnumpy().reshape(-1, 1)
              - 1
          )
          < 1e-4
      )
  
imports: import mxnet
  import numpy
  from gluonts.mx.distribution.gaussian import Gaussian
  from gluonts.mx.distribution.mixture import MixtureDistribution
  from gluonts.mx.distribution.uniform import Uniform
label_desc: Use image node_id 2 for calling a global function with example usage: test_inverse_quantile() without return types