code: def extract_static_embeddings(args, sents, vocab):
      processed = []
      if args.get("lowercase", True):  # handle word case
          case = lambda x: x.lower()
      else:
          case = lambda x: x
      for idx, sent in enumerate(sents):
          processed_sent = [vocab.map([case(w) for w in sent])]
          processed.append(processed_sent[0])
  
      words = get_long_tensor(processed, len(sents))
      words_mask = torch.eq(words, PAD_ID)
  
      return words, words_mask
  
imports: import os
  import logging
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence, pack_sequence, pad_sequence, PackedSequence
  from stanza.models.common.data import map_to_ids, get_long_tensor
  from stanza.models.common.exceptions import ForwardCharlmNotFoundError, BackwardCharlmNotFoundError
  from stanza.models.common.packed_lstm import PackedLSTM
  from stanza.models.common.dropout import WordDropout, LockedDropout
  from stanza.models.common.char_model import CharacterModel, CharacterLanguageModel
  from stanza.models.common.crf import CRFLoss
  from stanza.models.common.foundation_cache import load_bert
  from stanza.models.common.vocab import PAD_ID, UNK_ID, EMPTY_ID
  from stanza.models.common.bert_embedding import extract_bert_embeddings
label_desc: Use image node_id 5 for calling the NERTagger obj's underlying member method code with example usage: obj.extract_static_embeddings(args, sents, vocab) and returns: words,  words_mask