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 input_tensor(self) -> torch.Tensor:
      max_matrix_size = int(np.sqrt(self.tensor_size))
      input_tensor = torch.zeros(
          1,
          self.action_memory_len + 1,
          self.tensor_size,
          self.tensor_size,
          self.tensor_size,
      )
      matrix_dims = (
          torch.randint(1, max_matrix_size, (3,))
          .detach()
          .cpu()
          .numpy()
          .tolist()
      )
      operation_tensor = self._build_tensor_game_input(
          *matrix_dims, action_memory_len=self.action_memory_len
      )
  
      input_tensor[
          0,
          :,
          : operation_tensor.shape[1],
          : operation_tensor.shape[2],
          : operation_tensor.shape[3],
      ] = operation_tensor
      return input_tensor.to(self.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 10 for calling the TensorGameDataset obj's underlying member method code with example usage: obj.input_tensor() and returns: input_tensor