code: def get_features_dataframe(
      series: pd.Series,
      time_features: List[TimeFeature],
      lag_indices: List[int],
      past_data: Optional[pd.Series] = None,
  ) -> pd.DataFrame:
      """
      Constructs a DataFrame of features for a given Series.
  
      Features include some date-time features (like hour-of-day, day-of-week, ...) and
      lagged values from the series itself. Lag indices are specified by `lags`, while
      previous data can be specified by `past_data`: the latter allows to get lags also
      for the initial values of the series.
  
      Parameters
      ----------
      series
          Series on which features should be computed.
      time_features
          List of time features to be included in the data frame.
      lag_indices
          List of indices of lagged observations to be included as features.
      past_data
          Prior data, to be used to compute lagged observations.
  
      Returns
      -------
      pd.DataFrame
          A DataFrame containing the features. This has the same index as `series`.
      """
      # TODO check if anything can be optimized here
  
      assert (
          past_data is None or series.index.freq == past_data.index.freq
      )
      assert past_data is None or series.index[0] > past_data.index[-1]
  
      time_feature_columns = {
          feature.__name__: feature(series.index)
          for feature in time_features
      }
  
      all_data = (
          series
          if past_data is None
          else past_data.append(series)
          .resample(series.index.freq)
          .asfreq(series.index.freq)
      )
  
      lag_columns = {
          f"lag_{idx}": all_data.shift(idx)[series.index].values
          for idx in lag_indices
      }
  
      columns = {
          **time_feature_columns,
          **lag_columns,
          "target": series.values,
      }
  
      return pd.DataFrame(columns, index=series.index)
  
imports: from typing import Callable, Dict, List, Optional, Iterator, Iterable, Tuple
  import numpy
  import pandas
  import shutil
  from pathlib import Path
  from autogluon.tabular import TabularPredictor
  from gluonts.core.serde import dump_json, load_json
  from gluonts.dataset.util import to_pandas
  from gluonts.dataset.field_names import FieldName
  from gluonts.itertools import batcher
  from gluonts.model.forecast import SampleForecast, QuantileForecast, Forecast
  from gluonts.model.predictor import Predictor
  from gluonts.time_feature import TimeFeature
label_desc: Use image node_id 3 for calling a global function with example usage: get_features_dataframe(series, time_features, lag_indices, past_data) and returns: pd