code: def __init__(
      self,
      config_path: str,
      batch_size: int,
      max_length: int,
      tokenizer: CPMBeeTokenizer,
      max_depth: int = 16,
  ) -> None:
      self._q_cmd = multiprocessing.Queue()
      self._q_cmd_out = multiprocessing.Queue()
      self._q_data = multiprocessing.Queue(maxsize=1)
      self._packer = _MixedDatasetBatchPacker(
          batch_size, max_length, tokenizer, max_depth
      )
      self._p = multiprocessing.Process(
          target=_mixed_dataset_process,
          args=(
              config_path,
              self._q_cmd,
              self._q_cmd_out,
              self._q_data,
              bmt.rank(),
              bmt.world_size(),
              self._packer,
          ),
      )
      self._p.start()
      self._closed = False
  
imports: from collections import OrderedDict
  import multiprocessing
  import os
  from queue import Empty
  from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
  from typing_extensions import TypedDict
  from ...dataset import DistributedDataset
  from ...tokenizers import CPMBeeTokenizer
  from ...utils.config import load_dataset_config
  import numpy
  import time
  from numpy.typing import NDArray
  import torch
  import bmtrain
  import importlib.machinery
  import importlib.util
  import types
  import random
label_desc: Use image node_id 1 to create a new MixedDataset object  with example: obj = MixedDataset(config_path, batch_size, max_length, tokenizer, max_depth)