code: def test_backwards_compatibility():
      hps = {
          "freq": "D",
          "context_length": 5,
          "num_forking": 4,
          "prediction_length": 3,
          "quantiles": [0.5, 0.1],
          "epochs": 1,
          "num_batches_per_epoch": 1,
          "use_feat_dynamic_real": True,
          "use_past_feat_dynamic_real": True,
          "enable_encoder_dynamic_feature": True,
          "enable_decoder_dynamic_feature": True,
          "scaling": True,
          "scaling_decoder_dynamic_feature": True,
      }
  
      dataset_train, dataset_test = make_dummy_datasets_with_features(
          cardinality=[3, 10],
          num_feat_dynamic_real=2,
          num_past_feat_dynamic_real=4,
          freq=hps["freq"],
          prediction_length=hps["prediction_length"],
      )
  
      dataset_train = list(dataset_train)
      dataset_test = list(dataset_test)
  
      for dataset in dataset_train, dataset_test:
          for entry in dataset:
              entry["dynamic_feat"] = entry.pop("feat_dynamic_real")
  
      estimator = MQCNNEstimator.from_inputs(dataset_train, **hps)
  
      predictor = estimator.train(dataset_train, num_workers=None)
      forecasts = list(predictor.predict(dataset_test))
      assert len(forecasts) == len(dataset_test)
  
file_path: gluonts/test/mx/model/seq2seq/test_model.py
imports: import pytest
  from gluonts.mx import MQCNNEstimator, MQRNNEstimator
  from gluonts.mx.distribution import GaussianOutput, ISQFOutput
  from gluonts.testutil.dummy_datasets import make_dummy_datasets_with_features
label_desc: Use image node_id 10 for calling a global function with example usage: test_backwards_compatibility() without return types