code: def compute_data_from_recipe(
      self,
      num_steps: int,
      constant: Optional[float] = None,
      one_to_zero: float = 0.1,
      zero_to_one: float = 0.1,
      scale_features: float = 200,
  ) -> TrainDatasets:
      recipe = []
      recipe_type = Constant(constant)
      if self.is_noise:
          recipe_type += RandomGaussian()  # Use default stddev = 1.0
      if self.is_trend:
          recipe_type += LinearTrend()
      if self.is_promotions:
          recipe.append(
              (
                  "binary_causal",
                  BinaryMarkovChain(one_to_zero, zero_to_one),
              )
          )
          recipe.append(
              (FieldName.FEAT_DYNAMIC_REAL, Stack(["binary_causal"]))
          )
          recipe_type += scale_features * Lag("binary_causal", lag=0)
      if self.holidays:
          # Compute dates array
          dates = list(
              pd.period_range(
                  self.start, periods=num_steps, freq=self.freq
              )
          )
          recipe.append(
              ("binary_holidays", BinaryHolidays(dates, self.holidays))
          )
          recipe.append(
              (FieldName.FEAT_DYNAMIC_REAL, Stack(["binary_holidays"]))
          )
          recipe_type += scale_features * Lag("binary_holidays", lag=0)
      recipe.append((FieldName.TARGET, recipe_type))
      max_train_length = num_steps - self.prediction_length
      data = RecipeDataset(
          recipe=recipe,
          metadata=self.metadata,
          max_train_length=max_train_length,
          prediction_length=self.prediction_length,
          # Add 1 time series at a time in the loop for different constant
          # values per time series
          num_timeseries=1,
      )
      generated = data.generate()
      return generated
  
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 4 for calling the ConstantDataset obj's underlying member method code with example usage: obj.compute_data_from_recipe(num_steps, constant, one_to_zero, zero_to_one, scale_features) and returns: generated