code: def test_tsdataset_roll_single_id(self):
      df = get_ts_df()
      horizon = random.randint(1, 10)
      lookback = random.randint(1, 20)
  
      tsdata = TSDataset.from_pandas(
          df,
          dt_col="datetime",
          target_col="value",
          extra_feature_col=["extra feature"],
          id_col="id",
      )
  
      with pytest.raises(RuntimeError):
          tsdata.to_numpy()
  
      # roll train, diff input.
      tsdata.roll(lookback=lookback, horizon=horizon)
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 2)
      assert y.shape == (len(df) - lookback - horizon + 1, horizon, 1)
  
      tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=True)
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 2)
      assert y.shape == (len(df) - lookback - horizon + 1, horizon, 1)
  
      # add extra_feature_col.
      tsdata.roll(
          lookback=lookback,
          horizon=horizon,
          feature_col=["extra feature"],
          target_col="value",
      )
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 2)
      assert y.shape == (len(df) - lookback - horizon + 1, horizon, 1)
  
      tsdata.roll(
          lookback=lookback,
          horizon=horizon,
          feature_col=["extra feature"],
          target_col="value",
          id_sensitive=True,
      )
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 2)
      assert y.shape == (len(df) - lookback - horizon + 1, horizon, 1)
  
      tsdata.roll(
          lookback=lookback,
          horizon=horizon,
          feature_col=[],
          target_col="value",
      )
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 1)
      assert y.shape == (len(df) - lookback - horizon + 1, horizon, 1)
  
      tsdata.roll(
          lookback=lookback,
          horizon=horizon,
          feature_col=[],
          target_col="value",
          id_sensitive=True,
      )
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 1)
      assert y.shape == (len(df) - lookback - horizon + 1, horizon, 1)
  
      # roll test.
      horizon = 0
      lookback = random.randint(1, 20)
  
      tsdata.roll(lookback=lookback, horizon=horizon)
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 2)
      assert y is None
  
      tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=True)
      x, y = tsdata.to_numpy()
      assert x.shape == (len(df) - lookback - horizon + 1, lookback, 2)
      assert y is None
      tsdata._check_basic_invariants()
  
imports: import pytest
  import numpy
  import pandas
  import random
  import tempfile
  import os
  import shutil
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.chronos.data import TSDataset
  from pandas.testing import assert_frame_equal
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 6 for calling the TestTSDataset obj's underlying member method code with example usage: obj.test_tsdataset_roll_single_id() without return types