code: def test_symbol_and_array(hybridize: bool):
      # Tests for cases like the one presented in issue 1211, in which the Inflated
      # Beta outputs used a method only available to arrays and not to symbols.
      # We simply go through a short training to ensure no exceptions are raised.
      data = [
          {
              "target": [0, 0.0460043, 0.263906, 0.4103112, 1],
              "start": pd.to_datetime("1999-01-04"),
          },
          {
              "target": [1, 0.65815564, 0.44982578, 0.58875054, 0],
              "start": pd.to_datetime("1999-01-04"),
          },
      ]
      dataset = common.ListDataset(
          data, freq="W-MON", one_dim_target=True
      )
  
      trainer = Trainer(
          epochs=1, num_batches_per_epoch=2, hybridize=hybridize
      )
  
      estimator = DeepAREstimator(
          freq="W",
          prediction_length=2,
          trainer=trainer,
          distr_output=ZeroAndOneInflatedBetaOutput(),
          context_length=2,
          batch_size=1,
          scaling=False,
      )
  
      estimator.train(dataset)
  
imports: import pandas
  import pytest
  from gluonts.dataset import common
  from gluonts.mx import DeepAREstimator
  from gluonts.mx.distribution.inflated_beta import ZeroAndOneInflatedBetaOutput
  from gluonts.mx.trainer import Trainer
label_desc: Use image node_id 1 for calling a global function with example usage: test_symbol_and_array(hybridize) without return types