code: def transform(self, data: DataEntry) -> DataEntry:
      past_length_bottom_ts = data[f"past_{FieldName.FEAT_TIME}"].shape[
          1
      ]
  
      agg_features_dict = {}
      for i, (agg_multiple, agg_estimator) in enumerate(
          zip(self.agg_multiples, self.agg_estimators)
      ):
          (
              past_time_feat_agg,
              future_time_feat_agg,
          ) = self._get_time_features_agg_level(
              forecast_start=data[FieldName.FORECAST_START],
              past_length_bottom_ts=past_length_bottom_ts,
              agg_estimator=agg_estimator,
          )
          agg_features_dict[f"level_{i}"] = {
              "past_time_feat_agg": past_time_feat_agg,
              "future_time_feat_agg": future_time_feat_agg,
          }
          data.update({"agg_features_dict": agg_features_dict})
      return data
  
imports: from copy import deepcopy
  from functools import partial
  from typing import List, Tuple, Optional
  from mxnet.gluon import HybridBlock
  import numpy
  import pandas
  from gluonts.core.component import Type, validated
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import DataLoader, TrainDataLoader, ValidationDataLoader
  from gluonts.model.predictor import Predictor
  from gluonts.mx.distribution import GaussianOutput
  from gluonts.mx.model.estimator import GluonEstimator
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import get_hybrid_forward_input_names
  from gluonts.mx.util import copy_parameters
  from gluonts.time_feature import get_lags_for_frequency, time_features_from_frequency_str
  from gluonts.transform import SelectFields, SimpleTransformation, Transformation, MissingValueImputation, RollingMeanValueImputation
  from gluonts.nursery.temporal_hierarchical_forecasting.model.cop_deepar.gluonts_fixes import batchify_with_dict, DeepAREstimatorForCOP, LOSS_FUNCTIONS, RepresentableBlockPredictorBatchifyWithDict
  from gluonts.nursery.temporal_hierarchical_forecasting.utils.common import TEMPORAL_HIERARCHIES
  from gluonts.nursery.temporal_hierarchical_forecasting.model.cop_deepar._network import COPDeepARTrainingNetwork, COPDeepARPredictionNetwork
label_desc: Use image node_id 2 for calling the AddTimeFeaturesAtAggregateLevels obj's underlying member method code with example usage: obj.transform(data) and returns: data