code: def get_group_data(name):
      dataset = get_dataset(name)
      dataset_group = []
      it = iter(dataset.train)
      num_ts = int(dataset.metadata.feat_static_cat[0].cardinality)
      for i in range(num_ts):
          train_entry = next(it)
          dataset_group.append(
              ListDataset(
                  [
                      {
                          "target": train_entry["target"],
                          "start": train_entry["start"],
                      }
                  ],
                  freq=dataset.metadata.freq,
              )
          )
      return dataset_group
  
imports: from pts.dataset.repository.datasets import get_dataset, dataset_recipes
  from pts.dataset.utils import to_pandas
  import numpy
  import pandas
  from pts.dataset import ListDataset
  import torch
  from pts.model.simple_feedforward import SimpleFeedForwardEstimator
  import pickle
  import json
  import random
label_desc: Use image node_id 8 for calling a global function with example usage: get_group_data(name) and returns: dataset_group