class_docstr: 经过处理的数据信息，包括一系列数据集（比如：分开的训练集、验证集和测试集）以及各个 field 对应的 vocabulary。该对象一般由
  **fastNLP** 中各种 :class:`~fastNLP.io.loader.Loader` 的 :meth:`load` 函数生成，可以通过以下的方法获取里面的内容::
  
      data_bundle = YelpLoader().load({'train':'/path/to/train', 'dev': '/path/to/dev'})
      train_vocabs = data_bundle.vocabs['train']
      train_data = data_bundle.datasets['train']
      dev_data = data_bundle.datasets['train']
  
  :param vocabs: 从名称（字符串）到 :class:`~fastNLP.core.Vocabulary` 类型的字典
  :param datasets: 从名称（字符串）到 :class:`~fastNLP.core.dataset.DataSet` 类型的字典。建议不要将相同的 ``DataSet`` 对象重复传入，
      否则可能会在使用 :class:`~fastNLP.io.pipe.Pipe` 处理数据的时候遇到问题，若多个数据集确需一致，请手动 ``deepcopy`` 后传入。
docstr: 获取名为 ``name`` 的 dataset
  
  :param name: dataset的名称，一般为 'train', 'dev', 'test' 。
  :return:
docstr_tok: ["\u83b7\u53d6\u540d\u4e3a","``","name","``","\u7684","dataset",":","param","name",":","dataset\u7684\u540d\u79f0\uff0c\u4e00\u822c\u4e3a","'train","'",",","'dev","'",",","'test","'","\u3002",":","return",":"]
code: def get_dataset(self, name: str) -> DataSet:
      r"""
      获取名为 ``name`` 的 dataset
  
      :param name: dataset的名称，一般为 'train', 'dev', 'test' 。
      :return:
      """
      if name in self.datasets.keys():
          return self.datasets[name]
      else:
          error_msg = (
              f"DataBundle do NOT have DataSet named {name}. "
              f"It should be one of {self.datasets.keys()}."
          )
          logger.error(error_msg)
          raise KeyError(error_msg)
  
imports: from typing import Union, List, Callable, Dict
  from ..core.dataset import DataSet
  from fastNLP.core.vocabulary import Vocabulary
  from fastNLP.core import logger
label_desc: Use image node_id 6 for calling the DataBundle obj's underlying member method code with example usage: obj.get_dataset(name) and returns: self