class_docstr: A data module which provides an artificial dataset.
code: def generate(self) -> None:
      meta = MetaData(
          context_length_multiple=self.context_length_multiple,
          support_length_multiple=self.support_length_multiple,
          support_set_size=self.support_set_size,
          prediction_length=self.prediction_length,
          num_queries=self.num_queries,
          freq="1H",
      )
      if self.root.exists():
          # check if the meta data of the dataset fits the requirements
          if self.meta == meta:
              return
          else:
              raise ValueError(
                  "Meta data of artificial dataset not compatible with requirements"
              )
  
      self.root.mkdir(parents=True, exist_ok=True)
      meta.save(self.root / "metadata.json")
      for split, n_samples in zip(
          ["train", "val", "test"],
          [self.train_set_size, self.val_set_size, self.test_set_size],
      ):
          self.generate_split(split, n_samples)
  
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 11 for calling the ArtificialDataModule obj's underlying member method code with example usage: obj.generate() without return types