code: def generate_ts(
      self, num_ts_steps: int, is_train: bool = False
  ) -> List[DataEntry]:
      res = []
      constant = None
      for i in range(self.num_timeseries):
          if self.is_nan:
              target = self.insert_nans_and_zeros(num_ts_steps)
          elif self.is_piecewise:
              target = self.piecewise_constant(i, num_ts_steps)
          else:
              constant = self.determine_constant(i, constant)
              if self.num_missing_middle > 0:
                  target = self.insert_missing_vals_middle(
                      num_ts_steps, constant
                  )
              elif (
                  self.is_noise
                  or self.is_trend
                  or self.is_promotions
                  or self.holidays
              ):
                  num_steps = self.get_num_steps(i)
                  generated = self.compute_data_from_recipe(
                      num_steps, constant
                  )
                  if is_train:
                      time_series = generated.train
                  else:
                      assert generated.test is not None
                      time_series = generated.test
                  # returns np array convert to list for consistency
                  target = list(time_series)[0][
                      FieldName.TARGET
                  ].tolist()
              else:
                  target = [constant] * num_ts_steps
          ts_data = dict(
              start=self.start,
              target=target,
              item_id=str(i),
              feat_static_cat=[i],
              feat_static_real=[i],
          )
          if self.is_promotions or self.holidays:
              ts_data[FieldName.FEAT_DYNAMIC_REAL] = list(time_series)[
                  0
              ][FieldName.FEAT_DYNAMIC_REAL].tolist()
          res.append(ts_data)
      return res
  
imports: import math
  import random
  from abc import abstractmethod
  from typing import Callable, Dict, List, NamedTuple, Optional, Tuple, Union, cast
  import numpy
  import pandas
  from pandas.tseries.frequencies import to_offset
  from pandas.tseries.offsets import BaseOffset, Week
  from gluonts.dataset.artificial.recipe import BinaryHolidays, BinaryMarkovChain, Constant, ForEachCat, Lag, LinearTrend, RandomCat, RandomGaussian, Stack, generate, take_as_list
  from gluonts.dataset.common import BasicFeatureInfo, CategoricalFeatureInfo, DataEntry, Dataset, ListDataset, MetaData, TrainDatasets
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.stat import DatasetStatistics, calculate_dataset_statistics
label_desc: Use image node_id 9 for calling the ConstantDataset obj's underlying member method code with example usage: obj.generate_ts(num_ts_steps, is_train) and returns: res