class_docstr: Dataset to be used for training the AlphaTensor algorithm using both
  actor generated and synthetic data. A basis change can be applied to both
  the data type with a probability specified in the constructor. The
  synthetic data and the actor generated one are stored in two data buffers.
code: def add_best_game(
      self,
      states: List[torch.Tensor],
      policies: List[torch.Tensor],
      rewards: List[torch.Tensor],
  ):
      self.best_game_data_buffer.add_game(states, policies, rewards)
  
imports: import json
  import os
  import shutil
  import tempfile
  from pathlib import Path
  from typing import List, Tuple
  import numpy
  import torch
  from torch.utils.data import Dataset
  from open_alpha_tensor.core.data.generation import generate_synthetic_data
  from open_alpha_tensor.core.data.utils import get_scalars, map_triplet_to_action
label_desc: Use image node_id 7 for calling the TensorGameDataset obj's underlying member method code with example usage: obj.add_best_game(states, policies, rewards) without return types