code: def generate_artificial_tuplets(
      self,
      item_id: int,
      always_cheat: bool,
      query_length_scale: float,
      counterfactual_size: int,
  ) -> Tuple:
      assert always_cheat is True, "In CounterFactual, we always cheat"
      assert self.meta.support_length_scale_same_as_query is True
  
      # generate query
      query_length = self.meta.prediction_length + self.context_length
      query_x = np.arange(query_length).reshape(-1, 1)
      support_x = np.arange(self.support_length).reshape(-1, 1)
  
      kernel = 0.5 * RBF(
          length_scale=query_length_scale,
          length_scale_bounds=(1e-1, 10.0),
      )
      gpr = GaussianProcessRegressor(
          kernel=kernel, random_state=self.random_state
      )
      query = gpr.sample_y(
          query_x, 1, random_state=self.random_state
      ).squeeze()
      assert query.shape == (query_length,), query.shape
  
      support_set = gpr.sample_y(
          support_x,
          self.meta.support_set_size,
          random_state=self.random_state,
      ).T
  
      assert support_set.shape == (
          self.meta.support_set_size,
          self.support_length,
      ), support_set.shape
  
      # generate a random place to for TS in support set to start cheating
      marker_length = self.meta.prediction_length
      signal_length = marker_length + self.meta.prediction_length
      marker_start = self.random_state.choice(
          np.arange(0, self.support_length - signal_length)
      )
  
      # prepare data to train the GP
      x = np.array(
          range(marker_start, marker_start + signal_length)
      ).reshape(-1, 1)
      y = query.tolist()[-signal_length:]
  
      # fit gp and sample from fitted gp
      gpr.fit(x, y)
  
      # generate a random index from support set to cheat
      si = self.random_state.choice(self.meta.support_set_size)
      y_hat = gpr.sample_y(support_x, 1, random_state=self.random_state)
      support_set[si] = y_hat.ravel()
  
      indices = list(range(self.meta.support_set_size))
      indices.remove(si)
  
      # generate a counterfactual TS, only contains information in the context (marker length)
      x_cf = np.array(
          range(marker_start, marker_start + marker_length)
      ).reshape(-1, 1)
      y_cf = query.tolist()[-signal_length:-marker_length]
  
      # fit gp and sample from fitted gp
      for _ in range(counterfactual_size):
          gpr_cf = GaussianProcessRegressor(
              kernel=kernel, random_state=self.random_state
          )
          gpr_cf.fit(x_cf, y_cf)
          # generate a random index from support set for cf
          cf_si = self.random_state.choice(indices)
          y_hat_cf = gpr_cf.sample_y(
              support_x, 1, random_state=self.random_state
          )
          support_set[cf_si] = y_hat_cf.ravel()
          indices.remove(cf_si)
  
      # add noise
      if self.meta.noise_level > 0:
          noise = (
              self.random_state.randn(query.shape[0])
              * self.meta.noise_level
          )
          query += noise
  
          new_support_set = []
          for s in support_set:
              noise = (
                  self.random_state.randn(s.shape[0])
                  * self.meta.noise_level
              )
              new_support_set.append(s + noise)
          support_set = np.array(new_support_set)
  
      q = {
          "target": query.tolist(),
          "item_id": item_id,
          "start": "2012-01-01 00:00:00",
      }
      s = [
          {
              "target": s.tolist(),
              "start": "2012-01-01 00:00:00",
              "item_id": item_id,
          }
          for s in support_set
      ]
      return q, s
  
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 8 for calling the CheatCounterfactualArtificialDataModule obj's underlying member method code with example usage: obj.generate_artificial_tuplets(item_id, always_cheat, query_length_scale, counterfactual_size) and returns: q,  s