class_docstr: Hashing data storage for datahanlder
  - The default data storage pandas.DataFrame is too slow when randomly accessing one stock's data
  - HashingStockStorage hashes the multiple stocks' data(pandas.DataFrame) by the key `stock_id`.
  - HashingStockStorage hashes the pandas.DataFrame into a dict, whose key is the stock_id(str) and value this stock data(panda.DataFrame), it has the following format:
      {
          stock1_id: stock1_data,
          stock2_id: stock2_data,
          ...
          stockn_id: stockn_data,
      }
  - By the `fetch` method, users can access any stock data with much lower time cost than default data storage
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docstr: the arg `proc_func` in `fetch` method is not supported in HashingStockStorage
docstr_tok: ["the","arg","`","proc_func","`","in","`","fetch","`","method","is","not","supported","in","HashingStockStorage"]
code: def is_proc_func_supported(self):
      """the arg `proc_func` in `fetch` method is not supported in HashingStockStorage"""
      return False
  
file_path: qlib/qlib/data/dataset/storage.py
imports: import pandas
  import numpy
  from .handler import DataHandler
  from typing import Union, List, Callable
  from .utils import get_level_index, fetch_df_by_index, fetch_df_by_col
label_desc: Use image node_id 5 for calling the HashingStockStorage obj's underlying member method code with example usage: obj.is_proc_func_supported() and returns: False