class_docstr: A data module which provides an artificial dataset.
code: def generate_split(
      self, split: Literal["train", "val", "test"], n_samples: int
  ) -> None:
      queries, support_sets = zip(
          *[
              generate_artificial_tuplets(
                  dataset_name=self.dataset_name,
                  context_length=self.context_length,
                  support_length=self.support_length,
                  prediction_length=self.prediction_length,
                  support_set_size=self.support_set_size,
                  item_id=i,
              )
              for i in tqdm(
                  range(n_samples), desc="generating artificial data"
              )
          ]
      )
      _write_data_to_file(self.root / split / "data.json", queries)
      _write_data_to_file(
          self.root / split / ".support_set.json", support_sets
      )
  
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 12 for calling the ArtificialDataModule obj's underlying member method code with example usage: obj.generate_split(split, n_samples) without return types