code: def __init__(
      self,
      num_timeseries: int = 10,
      num_steps: int = 30,
      freq: str = "1H",
      start: str = "2000-01-01 00:00:00",
      # Generates constant dataset of 0s with explicit NaN missing values
      is_nan: bool = False,
      # Inserts random constant value for each time series
      is_random_constant: bool = False,
      # Generates constants on various scales
      is_different_scales: bool = False,
      # Determines whether the time series in the test
      # and train set should have different constant values
      is_piecewise: bool = False,
      # Determines whether to add Gaussian noise to the constant dataset
      is_noise: bool = False,
      # Determines whether some time series will have very long lengths
      is_long: bool = False,
      # Determines whether some time series will have very short lengths
      is_short: bool = False,
      # Determines whether to add linear trends
      is_trend: bool = False,
      # Number of missing values in the middle of the time series
      num_missing_middle: int = 0,
      # Determines whether to add promotions to the target time series
      # and to store in metadata
      is_promotions: bool = False,
      # Determines whether to add holidays to the target time series
      # and to store in metadata
      holidays: Optional[List[pd.Timestamp]] = None,
  ) -> None:
      super().__init__(freq)
      self.num_timeseries = num_timeseries
      self.num_steps = num_steps
      self.num_training_steps = self.num_steps // 10 * 8
      self.prediction_length = self.num_steps - self.num_training_steps
      self.is_nan = is_nan
      self.is_random_constant = is_random_constant
      self.is_different_scales = is_different_scales
      self.is_piecewise = is_piecewise
      self.is_noise = is_noise
      self.is_long = is_long
      self.is_short = is_short
      self.is_trend = is_trend
      self.num_missing_middle = num_missing_middle
      self.is_promotions = is_promotions
      self.holidays = holidays
  
      if isinstance(self.freq, Week):
          self.freq = Week(
              self.freq.n, weekday=pd.Timestamp(start).weekday()
          )
      self.start = cast(pd.Period, pd.Period(start, self.freq))
  
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 1 to create a new ConstantDataset object from inherited base classes: ArtificialDataset with example: obj = ConstantDataset(num_timeseries, num_steps, freq, start, is_nan, is_random_constant, is_different_scales, is_piecewise, is_noise, is_long, is_short, is_trend, num_missing_middle, is_promotions, holidays)