code: def test_gp_output():
      # test that gp output gives expected shapes
      batch = 1
      hidden_size = 3
      dim = 4
      rank = 2
  
      states = mx.ndarray.ones(shape=(batch, dim, hidden_size))
  
      lowrank_gp_output = LowrankGPOutput(dim=dim, rank=rank)
  
      proj = lowrank_gp_output.get_args_proj()
  
      proj.initialize()
  
      distr_args = proj(states)
  
      mu, D, W = distr_args
  
      assert mu.shape == (batch, dim)
      assert D.shape == (batch, dim)
      assert W.shape == (batch, dim, rank)
  
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 2 for calling a global function with example usage: test_gp_output() without return types