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 field 名为 ``field_name`` 对应的词表
  
  :param field_name: 名称
  :return: :class:`~fastNLP.core.Vocabulary`
docstr_tok: ["\u83b7\u53d6","field","\u540d\u4e3a","``","field_name","``","\u5bf9\u5e94\u7684\u8bcd\u8868",":","param","field_name",":","\u540d\u79f0",":","return",":",":","class",":","`","~fastNLP.core.Vocabulary","`"]
code: def get_vocab(self, name: str) -> Vocabulary:
      r"""
      获取 field 名为 ``field_name`` 对应的词表
  
      :param field_name: 名称
      :return: :class:`~fastNLP.core.Vocabulary`
      """
      if name in self.vocabs.keys():
          return self.vocabs[name]
      else:
          error_msg = (
              f"DataBundle do NOT have Vocabulary named {name}. "
              f"It should be one of {self.vocabs.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 8 for calling the DataBundle obj's underlying member method code with example usage: obj.get_vocab(name) and returns: self