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 __getitem__(self, idx):
      if self.synth_bool[idx]:
          return self.synthetic_data_buffer[
              self.synth_idx[self.synth_bool[:idx].sum()]
          ]
      else:
          if self.pct_best_game > 0 and self.best_game_idx is not None:
              if idx - self.synth_bool[:idx].sum() < len(
                  self.best_game_idx
              ):
                  return self.best_game_data_buffer[
                      self.best_game_idx[
                          idx - self.synth_bool[:idx].sum()
                      ]
                  ]
              else:
                  return self.game_data_buffer[
                      self.game_idx[
                          idx
                          - self.synth_bool[:idx].sum()
                          - len(self.best_game_idx)
                      ]
                  ]
          else:
              return self.game_data_buffer[
                  self.game_idx[idx - self.synth_bool[:idx].sum()]
              ]
  
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 4 for calling the TensorGameDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: self, self, self, self