code: def extend_from_instances(self,
                                params: Params,
                                instances: Iterable['adi.Instance'] = ()) -> None:
          
          min_count = params.pop("min_count", None)
          max_vocab_size = pop_max_vocab_size(params)
          non_padded_namespaces = params.pop("non_padded_namespaces", DEFAULT_NON_PADDED_NAMESPACES)
          pretrained_files = params.pop("pretrained_files", {})
          min_pretrained_embeddings = params.pop("min_pretrained_embeddings", None)
          only_include_pretrained_words = params.pop_bool("only_include_pretrained_words", False)
          tokens_to_add = params.pop("tokens_to_add", None)
          params.assert_empty("Vocabulary - from dataset")
  
          logger.info("Fitting token dictionary from dataset.")
          namespace_token_counts: Dict[str, Dict[str, int]] = defaultdict(lambda: defaultdict(int))
          for instance in Tqdm.tqdm(instances):
              instance.count_vocab_items(namespace_token_counts)
          self._extend(counter=namespace_token_counts,
                       min_count=min_count,
                       max_vocab_size=max_vocab_size,
                       non_padded_namespaces=non_padded_namespaces,
                       pretrained_files=pretrained_files,
                       only_include_pretrained_words=only_include_pretrained_words,
                       tokens_to_add=tokens_to_add,
                       min_pretrained_embeddings=min_pretrained_embeddings)
docs: Extends an already generated vocabulary using a collection of instances.