code: def __init__(
      self,
      # dataset metadata config starts
      context_length_multiple: int = 4,
      prediction_length: int = 24,
      support_length_multiple: int = 4,
      test_set_size: int = 500,
      val_set_size: int = 500,
      train_set_size: int = 10000,
      freq: str = "H",
      support_set_size: int = 3,
      cheat_chance: float = 1.0,
      noise_level: float = 0.0,
      query_length_scale: float = 5.0,
      support_length_scale_same_as_query: bool = True,
      counterfactual_size: int = 1,
      counterfactual_mixing: bool = False,
      # data loader config starts
      data_dir: Path = Path.home() / ".mxnet" / "gluon-ts",
      standardize: bool = False,
      batch_size_train: int = 128,
      batch_size_val_test: int = 1000,
      num_workers: int = 0,
      random_seed: int = int.from_bytes(os.urandom(3), byteorder="big"),
  ):
      assert 0.1 <= query_length_scale <= 10
      assert counterfactual_size < support_set_size
      super().__init__()
  
      # parameters of the dataset
      self.meta = CheatCounterfactual(
          context_length_multiple=context_length_multiple,
          prediction_length=prediction_length,
          support_length_multiple=support_length_multiple,
          test_set_size=test_set_size,
          val_set_size=val_set_size,
          train_set_size=train_set_size,
          freq=freq,
          support_set_size=support_set_size,
          cheat_chance=cheat_chance,
          noise_level=noise_level,
          query_length_scale=query_length_scale,
          support_length_scale_same_as_query=support_length_scale_same_as_query,
          counterfactual_size=counterfactual_size,
          counterfactual_mixing=counterfactual_mixing,
      )
  
      # parameters of the loader
      self.data_dir = data_dir
      self.standardize = standardize
      self.batch_size_train = batch_size_train
      self.batch_size_val_test = batch_size_val_test
      self.num_workers = num_workers
      self.splits: DatasetSplits
      self.random_state = np.random.RandomState(random_seed)
  
imports: import os
  from dataclasses import dataclass, asdict
  from typing import Optional, Literal, Tuple, List, Dict
  from pathlib import Path
  import json
  from tqdm import tqdm
  import hashlib
  import numpy
  import torch
  import torch.nn
  import pytorch_lightning
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import RBF
  from torch.utils.data import DataLoader, ConcatDataset
  from gluonts.time_feature import get_seasonality
  from .artificial import _write_data_to_file
  from meta.datasets.registry import register_data_module
  from meta.data.batch import TripletBatch
  from meta.datasets.splits import DatasetSplits
  from meta.data.sampling import TripletDataset
  from meta.common.torch import tensor_to_np
  from meta.metrics.numpy import compute_metrics
label_desc: Use image node_id 1 to create a new CheatCounterfactualArtificialDataModule object from inherited base classes: CheatArtificialDataModule with example: obj = CheatCounterfactualArtificialDataModule(context_length_multiple, prediction_length, support_length_multiple, test_set_size, val_set_size, train_set_size, freq, support_set_size, cheat_chance, noise_level, query_length_scale, support_length_scale_same_as_query, counterfactual_size, counterfactual_mixing, data_dir, standardize, batch_size_train, batch_size_val_test, num_workers, random_seed)