code: def metadata(self) -> MetaData:
      metadata = MetaData(
          freq=self.freq.freqstr,
          feat_static_cat=[
              CategoricalFeatureInfo(
                  name="feat_static_cat_000",
                  cardinality=str(self.num_timeseries),
              )
          ],
          feat_static_real=[
              BasicFeatureInfo(name="feat_static_real_000")
          ],
          prediction_length=self.prediction_length,
      )
      if self.is_promotions or self.holidays:
          metadata = MetaData(
              freq=self.freq.freqstr,
              feat_static_cat=[
                  CategoricalFeatureInfo(
                      name="feat_static_cat_000",
                      cardinality=str(self.num_timeseries),
                  )
              ],
              feat_static_real=[
                  BasicFeatureInfo(name="feat_static_real_000")
              ],
              feat_dynamic_real=[
                  BasicFeatureInfo(name=FieldName.FEAT_DYNAMIC_REAL)
              ],
              prediction_length=self.prediction_length,
          )
      return metadata
  
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 2 for calling the ConstantDataset obj's underlying member method code with example usage: obj.metadata() and returns: metadata