class_docstr: Generate sinus time series that ramp up and reach a certain amplitude, and
  level and have additional spikes on each sunday.
  
  TODO: This could be converted to a RecipeDataset to avoid code duplication.
class_docstr_tok: ["Generate","sinus","time","series","that","ramp","up","and","reach","a","certain","amplitude",",","and","level","and","have","additional","spikes","on","each","sunday",".","TODO",":","This","could","be","converted","to","a","RecipeDataset","to","avoid","code","duplication","."]
code: def train(self) -> List[DataEntry]:
      return [
          dict(
              start=ts[FieldName.START],
              target=ts[FieldName.TARGET][: -self.prediction_length],
              item_id=ts[FieldName.ITEM_ID],
          )
          for ts in self.make_timeseries()
      ]
  
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 6 for calling the ComplexSeasonalTimeSeries obj's underlying member method code with example usage: obj.train() and returns: unknown