class_docstr: A data module which provides an artificial dataset.
code: def train_dataloader(self) -> DataLoader[TripletBatch]:
      split = self.splits.train()
      return DataLoader(
          TripletDataset(
              queries=split.data(), support_sets=split.support_set()
          ),
          collate_fn=TripletBatch.collate,  # type: ignore
          batch_size=self.batch_size_train,
          num_workers=self.num_workers,
          pin_memory=True,
      )
  
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 8 for calling the ArtificialDataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: DataLoader