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我收到此错误，它阻止我的代码运行。我尝试过滤警告，但即便如此，它也会停止我的代码的运行。经过几个小时后，我仍然没有弄清楚如何克服它。

Là où les vêtements de sport connectés actuels sont axés sur la performance des sportifs, ici, on aura l'occasion pour des amateurs de se rassurer que les mouvements que nous effectuons sont justes. Cela nous évitera bien des mauvaises surprises (douleurs et autres...) au lendemain d'une activité.
Traceback (most recent call last):
  File "/gpfs7kw/linkhome/rech/genlig01/umg16uw/test/expe_5/substitution/augment.py", line 93, in <module>
    gen_eda(args.input, output, alpha_sr=alpha_sr, alpha_ri=alpha_ri, alpha_rs=alpha_rs, alpha_rd=alpha_rd, num_aug=num_aug)
  File "/gpfs7kw/linkhome/rech/genlig01/umg16uw/test/expe_5/substitution/augment.py", line 80, in gen_eda
    aug_sentences = eda(sentence, alpha_sr=alpha_sr, alpha_ri=alpha_ri, alpha_rs=alpha_rs, p_rd=alpha_rd, num_aug=num_aug)
  File "/gpfs7kw/linkhome/rech/genlig01/umg16uw/test/expe_5/substitution/substitution.py", line 229, in eda
    words = tokenizer(sentence)
  File "/gpfs7kw/linkhome/rech/genlig01/umg16uw/test/expe_5/substitution/substitution.py", line 60, in tokenizer
    sent_doc = nlp(sentence)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/spacy/language.py", line 998, in __call__
    doc = self.make_doc(text)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/spacy/language.py", line 1081, in make_doc
    return self.tokenizer(text)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/spacy_stanza/tokenizer.py", line 83, in __call__
    snlp_doc = self.snlp(text)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/stanza/pipeline/core.py", line 231, in __call__
    doc = self.process(doc)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/stanza/pipeline/core.py", line 225, in process
    doc = process(doc)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/stanza/pipeline/mwt_processor.py", line 33, in process
    preds += self.trainer.predict(b)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/stanza/models/mwt/trainer.py", line 79, in predict
    preds, _ = self.model.predict(src, src_mask, self.args['beam_size'])
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/stanza/models/common/seq2seq_model.py", line 296, in predict
    is_done = beam[b].advance(log_probs.data[b])
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/stanza/models/common/beam.py", line 86, in advance
    prevK = bestScoresId // numWords
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/torch/_tensor.py", line 29, in wrapped
    return f(*args, **kwargs)
  File "/linkhome/rech/genlig01/umg16uw/.conda/envs/bert/lib/python3.9/site-packages/torch/_tensor.py", line 575, in __floordiv__
    return torch.floor_divide(self, other)
UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at  /opt/conda/conda-bld/pytorch_1623448238472/work/aten/src/ATen/native/BinaryOps.cpp:467.)
Exception ignored in: <_io.FileIO name='Test_dolo_augmented.txt' mode='wb' closefd=True>
ResourceWarning: unclosed file <_io.TextIOWrapper name='Test_dolo_augmented.txt' mode='w' encoding='utf-8'>

这是我导入的库：


# -*- coding: UTF-8 -*-
# !/usr/bin/env python3

import random, pickle, os, csv
import re, string
import string
#import stanza
import spacy_stanza
import warnings
warnings.filterwarnings("error")
from random import shuffle

# stanza.download('fr')
nlp = spacy_stanza.load_pipeline('fr', processors='tokenize,mwt,pos,lemma')
random.seed(1)

def tokenizer(sentence):

    sent_doc = nlp(sentence)
    wds = [token.text for token in sent_doc if token.pos_ != 'SPACE']
    return wds
    
def lemmatizer(token):

    tok = [token.lemma_ for token in nlp(token)]
    tok_lemme = tok[0]
    #print(tok_lemme)
    
    return tok_lemme

test = "Là où les vêtements de sport connectés actuels sont axés sur la performance des sportifs, ici, on aura l'occasion pour des amateurs de se rassurer que les mouvements que nous effectuons sont justes. Cela nous évitera bien des mauvaises surprises (douleurs et autres...) au lendemain d'une activité."

tokenizer(test)

似乎问题与节有关，但我不知道为什么，我使用 pip 安装它应该卸载它吗？

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