code: def test_log_likelihood(
      ia_times, marks, valid_length, num_marks, loglike
  ):
      mx.rnd.seed(seed_state=1234)
  
      model = DeepTPPTrainingNetwork(
          num_marks=num_marks,
          interval_length=2,
          time_distr_output=WeibullOutput(),
      )
      model.initialize()
  
      loglike_pred = model(
          nd.stack(ia_times, marks, axis=-1), valid_length
      )
  
      assert loglike_pred.shape == (ia_times.shape[0],)
      assert _allclose(loglike, loglike_pred)
  
file_path: gluonts/test/mx/model/tpp/test_deeptpp.py
imports: import mxnet
  import numpy
  import pytest
  from mxnet import nd
  from gluonts.mx.model.tpp.deeptpp._network import DeepTPPPredictionNetwork, DeepTPPTrainingNetwork
  from gluonts.mx.model.tpp.distribution import WeibullOutput
label_desc: Use image node_id 2 for calling a global function with example usage: test_log_likelihood(ia_times, marks, valid_length, num_marks, loglike) without return types