code: def __init__(
      self,
      dataset_path: str,
      batch_size: int,
      max_length: int,
      tokenizer: CPMBeeTokenizer,
      max_depth: int = 16,
      task_name: str = "task",
      drop_last: bool = False,
  ) -> None:
      self._world_size = bmt.world_size()
      self._rank = bmt.rank()
      self._batch_size = batch_size
  
      self._packer = _MixedDatasetBatchPacker(
          batch_size * self._world_size,
          max_length,
          tokenizer,
          max_depth,
      )
      self._drop_last = drop_last
  
      ds = SimpleDataset(dataset_path, shuffle=False)
      self._ds_cfg: _MixedDatasetConfig = {
          "weight": 1.0,
          "path": dataset_path,
          "transforms": [],
          "task_name": task_name,
          "dataset_name": "finetune",
          "incontext_weight": [1.0],
          "lines": len(ds),
          "dataset": ds,
      }
  
file_path: DeepKE/example/llm/CPM-Bee/src/cpm_live/training_tasks/bee/finetune.py
imports: from ...tokenizers import CPMBeeTokenizer
  from .pretrain import _MixedDatasetBatchPacker, _MixedDatasetConfig, CPMBeeBatch
  from ...dataset import SimpleDataset
  import bmtrain
label_desc: Use image node_id 1 to create a new FinetuneDataset object  with example: obj = FinetuneDataset(dataset_path, batch_size, max_length, tokenizer, max_depth, task_name, drop_last)