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 __init__(
      self,
      len_data,
      pct_synth,
      tensor_size,
      n_synth_data,
      limit_rank,
      prob_distr,
      action_memory_len: int,
      device: str,
      n_steps: int,
      random_seed=None,
  ):
      self.synthetic_data_buffer = SyntheticDataBuffer(
          tensor_size,
          n_synth_data,
          limit_rank,
          prob_distr,
          action_memory_len,
          n_steps=n_steps,
          device=device,
          random_seed=random_seed,
      )
      self.game_data_buffer = GameDataBuffer(
          device=device, max_buffer_size=100000
      )
      self.best_game_data_buffer = GameDataBuffer(
          device=device, max_buffer_size=1000
      )
      self.len_data = len_data
      self.pct_synth = pct_synth
      self.pct_best_game = 0
      self.synth_bool = torch.ones(len_data, dtype=torch.bool)
      self.synth_idx = torch.from_numpy(
          np.random.choice(
              len(self.synthetic_data_buffer), len_data, replace=False
          )
      )
      self.game_idx = None
      self.best_game_idx = None
      self.action_memory_len = action_memory_len
      self.tensor_size = tensor_size
      self.device = device
  
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 1 to create a new TensorGameDataset object from inherited base classes: Dataset with example: obj = TensorGameDataset(len_data, pct_synth, tensor_size, n_synth_data, limit_rank, prob_distr, action_memory_len, device, n_steps, random_seed)