code: def from_json_stat(datasets, naming='label', value='value'):
      
  
      warnings.warn(
          "Shouldn't use this function anymore! Now use read() methods of"
          "Dataset, Collection or Dimension.",
          DeprecationWarning
      )
  
      check_input(naming)
      results = []
      if type(datasets) is list:
          for idx, element in enumerate(datasets):
              for dataset in element:
                  js_dict = datasets[idx][dataset]
                  results.append(generate_df(js_dict, naming, value))
      elif isinstance(datasets, OrderedDict) or type(datasets) is dict or \
              isinstance(datasets, Dataset):
          if 'class' in datasets:
              if datasets['class'] == 'dataset':
                  js_dict = datasets
                  results.append(generate_df(js_dict, naming, value))
          else:  # 1.00 bundle type
              for dataset in datasets:
                  js_dict = datasets[dataset]
                  results.append(generate_df(js_dict, naming, value))
      return results
docs: Decode JSON-stat formatted data into pandas.DataFrame object.
  
      Args:
        datasets(OrderedDict, list): data in JSON-stat format, previously \
                                     deserialized to a python object by \
                                     json.load() or json.loads(), for example.\
                                     Both List and OrderedDict are accepted \
                                     as inputs.
        naming(string, optional): dimension naming. Possible values: 'label'
                                  or 'id'.Defaults to 'label'.
        value (string, optional): name of the value column. Defaults to 'value'.
  
      Returns:
        results(list): list of pandas.DataFrame with imported data.