class_docstr: A data module which provides an artificial dataset.
code: def context_length(self) -> int:
      return self.context_length_multiple * self.meta.prediction_length
  
imports: from dataclasses import dataclass, asdict
  from typing import Optional, Literal, Tuple
  from pathlib import Path
  from tqdm.auto import tqdm
  import json
  import numpy
  import random
  import pytorch_lightning
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.repository.datasets import get_dataset, materialize_dataset
  from torch.utils.data import DataLoader
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import RBF
  import warnings
  from meta.data.batch import TripletBatch
  from meta.datasets.splits import DatasetSplits
  from meta.data.sampling import TripletDataset
  from meta.datasets.registry import register_data_module
label_desc: Use image node_id 2 for calling the ArtificialDataModule obj's underlying member method code with example usage: obj.context_length() and returns: unknown