code: def test_smoke(
      hybridize: bool,
      target_dim_sample: int,
      use_marginal_transformation: bool,
  ):
      num_batches_per_epoch = 1
      estimator = GPVAREstimator(
          distr_output=LowrankGPOutput(rank=2),
          num_cells=1,
          num_layers=1,
          pick_incomplete=True,
          prediction_length=metadata.prediction_length,
          target_dim=target_dim,
          target_dim_sample=target_dim_sample,
          freq=metadata.freq,
          use_marginal_transformation=use_marginal_transformation,
          trainer=Trainer(
              epochs=2,
              learning_rate=1e-4,
              num_batches_per_epoch=num_batches_per_epoch,
              hybridize=hybridize,
          ),
      )
  
      predictor = estimator.train(training_data=dataset.train)
  
      agg_metrics, _ = backtest_metrics(
          test_dataset=dataset.test,
          predictor=predictor,
          num_samples=10,
          evaluator=MultivariateEvaluator(
              quantiles=(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
          ),
      )
      assert agg_metrics["ND"] < 2.5
  
file_path: gluonts/test/mx/model/gpvar/test_gpvar.py
imports: import mxnet
  import pytest
  from flaky import flaky
  from gluonts.dataset.artificial import constant_dataset
  from gluonts.dataset.common import TrainDatasets
  from gluonts.dataset.multivariate_grouper import MultivariateGrouper
  from gluonts.evaluation import backtest_metrics, MultivariateEvaluator
  from gluonts.mx.model.gpvar import GPVAREstimator
  from gluonts.mx.distribution import LowrankMultivariateGaussian
  from gluonts.mx.distribution.lowrank_gp import GPArgProj, LowrankGPOutput
  from gluonts.mx.trainer import Trainer
label_desc: Use image node_id 4 for calling a global function with example usage: test_smoke(hybridize, target_dim_sample, use_marginal_transformation) without return types