code: def _prepare_train_data(self, dataset, funcs):
      logging.info("group training time-series to datasets")
      grouped_data = {}
      for key in funcs.keys():
          grouped_entry = [funcs[key](data)[key] for data in dataset]
  
          # we check that each time-series has the same length
          assert (
              len(set([len(x) for x in grouped_entry])) == 1
          ), f"alignement did not work as expected more than on length found: {set([len(x) for x in grouped_entry])}"
          grouped_data[key] = np.array(grouped_entry)
      if self.max_target_dimension is not None:
          # targets are often sorted by incr amplitude, use the last one when restricted number is asked
          grouped_data["target"] = grouped_data["target"][
              -self.max_target_dimension :, :
          ]
      grouped_data["item_id"] = "all_items"
      grouped_data["start"] = self.first_timestamp
      grouped_data["feat_static_cat"] = [0]
      return ListDataset(
          [grouped_data], freq=self.frequency, one_dim_target=False
      )
  
file_path: gluonts/src/gluonts/nursery/robust-mts-attack/multivariate/datasets/grouper.py
imports: import logging
  import numpy
  import pandas
  from gluonts.dataset.common import ListDataset, DataEntry, Dataset
label_desc: Use image node_id 7 for calling the Grouper obj's underlying member method code with example usage: obj._prepare_train_data(dataset, funcs) and returns: ListDataset