class_docstr: A data module which provides an artificial dataset.
code: def __init__(
      self,
      dataset_name: str,
      context_length_multiple: int,
      support_length_multiple: int,
      support_set_size: int,
      num_queries: int = 1,
      data_dir: Path = Path.home() / ".mxnet" / "gluont-ts",
      prediction_length: Optional[int] = None,
      test_set_size: int = 200,
      val_set_size: int = 200,
      train_set_size: int = 10000,
      standardize: bool = False,
      batch_size_train: int = 128,
      batch_size_val: int = 1000,
      batch_size_test: int = 1000,
      num_workers: int = 0,
      seed: Optional[int] = None,
  ):
      super().__init__()
      self.dataset_name = dataset_name
      self.data_dir = data_dir
      self.context_length_multiple = context_length_multiple
      self.support_length_multiple = support_length_multiple
      self._prediction_length = prediction_length
      self.test_set_size = test_set_size
      self.val_set_size = val_set_size
      self.train_set_size = train_set_size
      self.support_set_size = support_set_size
      self.num_queries = num_queries
      self.standardize = standardize
      self.splits: DatasetSplits
      self.batch_size_train = batch_size_train
      self.batch_size_val = batch_size_val
      self.batch_size_test = batch_size_test
      self.num_workers = num_workers
      self.seed = seed
  
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 1 to create a new ArtificialDataModule object from inherited base classes: pl with example: obj = ArtificialDataModule(dataset_name, context_length_multiple, support_length_multiple, support_set_size, num_queries, data_dir, prediction_length, test_set_size, val_set_size, train_set_size, standardize, batch_size_train, batch_size_val, batch_size_test, num_workers, seed)