code: def fit_mixture_distribution(
      x: Tensor,
      mdo: MixtureDistributionOutput,
      variate_dimensionality: int = 1,
      epochs: int = 1_000,
  ):
      args_proj = mdo.get_args_proj()
      args_proj.initialize()
      args_proj.hybridize()
  
      input = mx.nd.ones((variate_dimensionality, 1))
  
      trainer = mx.gluon.Trainer(
          args_proj.collect_params(), "sgd", {"learning_rate": 0.02}
      )
  
      t = tqdm(list(range(epochs)))
      for _ in t:
          with mx.autograd.record():
              distr_args = args_proj(input)
              d = mdo.distribution(distr_args)
              loss = d.loss(x).mean()
          loss.backward()
          loss_value = loss.asnumpy()
          t.set_postfix({"loss": loss_value})
          trainer.step(1)
  
      distr_args = args_proj(input)
      d = mdo.distribution(distr_args)
      return d
  
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 7 for calling a global function with example usage: fit_mixture_distribution(x, mdo, variate_dimensionality, epochs) and returns: d