code: def batch_geoparse(self, text_list):
          
          if not self.threads:
              print("batch_geoparsed should be used with threaded searches. Please set `threads=True` when initializing the geoparser.")
          nlped_docs = list(nlp.pipe(text_list, as_tuples=False, n_threads=multiprocessing.cpu_count()))
          processed = []
          for i in tqdm(nlped_docs, disable=not self.progress):
              p = self.geoparse(i)
              processed.append(p)
          return processed
docs: Batch geoparsing function. Take in a list of text documents and return a list of lists
          of the geoparsed documents. The speed improvements come exclusively from using spaCy's `nlp.pipe`.
  
          Parameters
          ----------
          text_list : list of strs
              List of documents. The documents should not have been pre-processed by spaCy.
  
          Returns
          -------
          processed : list of list of dictionaries.
              The list is the same length as the input list of documents. Each element is a list of dicts, one for
              each geolocated entity.