code: def test_short_history_item_pred():
      prediction_length = 7
      freq = "D"
  
      dataset = ListDataset(
          data_iter=[
              {
                  "start": "2017-10-11",
                  "item_id": "item_1",
                  "target": np.array(
                      [
                          1.0,
                          9.0,
                          2.0,
                          0.0,
                          0.0,
                          1.0,
                          5.0,
                          3.0,
                          4.0,
                          2.0,
                          0.0,
                          0.0,
                          1.0,
                          6.0,
                      ]
                  ),
                  "feat_static_cat": np.array([0.0, 0.0], dtype=float),
                  "past_feat_dynamic_real": np.array(
                      [
                          [1.0222e06 for i in range(14)],
                          [750.0 for i in range(14)],
                      ]
                  ),
              },
              {
                  "start": "2017-10-11",
                  "item_id": "item_2",
                  "target": np.array([7.0, 0.0, 0.0, 23.0, 13.0]),
                  "feat_static_cat": np.array([0.0, 1.0], dtype=float),
                  "past_feat_dynamic_real": np.array(
                      [[0 for i in range(5)], [750.0 for i in range(5)]]
                  ),
              },
          ],
          freq=freq,
      )
  
      predictor = TreePredictor(
          freq=freq,
          prediction_length=prediction_length,
          quantiles=[0.1, 0.5, 0.9],
          max_n_datapts=50000,
          method="QuantileRegression",
          use_past_feat_dynamic_real=True,
          use_feat_dynamic_real=False,
          use_feat_dynamic_cat=False,
          use_feat_static_real=False,
          cardinality="auto",
      )
      predictor = predictor.train(dataset)
      forecasts = list(predictor.predict(dataset))
      assert forecasts[1].quantile(0.5).shape[0] == prediction_length
  
imports: import pytest
  import numpy
  from gluonts.ext.rotbaum import TreeEstimator, TreePredictor
  from gluonts.testutil.dummy_datasets import make_dummy_datasets_with_features
  from gluonts.dataset.common import ListDataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_short_history_item_pred() without return types