class_docstr: Class for syntax parsing using Slovnet library
class_docstr_tok: ["Class","for","syntax","parsing","using","Slovnet","library"]
code: def preprocess_sentences(self, sentences, entity_offsets_batch):
      sentences_tokens_batch, replace_dict_batch = [], []
      for sentence, entity_offsets in zip(
          sentences, entity_offsets_batch
      ):
          if sentence.islower():
              for start, end in entity_offsets:
                  entity_old = sentence[start:end]
                  if entity_old:
                      entity_new = (
                          f"{entity_old[0].upper()}{entity_old[1:]}"
                      )
                      sentence = sentence.replace(
                          entity_old, entity_new
                      )
              sentence = f"{sentence[0].upper()}{sentence[1:]}"
          names3 = re.findall(
              r"([\w]{1}\.)([ ]?)([\w]{1}\.)([ ])([\w]{3,})", sentence
          )
          replace_dict = {}
          for name in names3:
              names_str = "".join(name)
              replace_dict[name[-1]] = (names_str, "name")
              sentence = sentence.replace(names_str, name[-1])
          names2 = re.findall(r"([\w]{1}\.)([ ])([\w]{3,})", sentence)
          for name in names2:
              names_str = "".join(name)
              replace_dict[name[-1]] = (names_str, "name")
              sentence = sentence.replace(names_str, name[-1])
          works_of_art = re.findall(r'(["«])(.*?)(["»])', sentence)
          for symb_start, work_of_art, symb_end in works_of_art:
              work_of_art_tokens = re.findall(
                  self.re_tokenizer, work_of_art
              )
              if len(work_of_art.split()) > 1:
                  short_substr = ""
                  for tok in work_of_art_tokens:
                      if self.nlp(tok)[0].pos_ == "NOUN":
                          short_substr = tok
                          break
                  if not short_substr:
                      short_substr = work_of_art_tokens[0]
                  replace_dict[short_substr] = (work_of_art, "name")
                  sentence = sentence.replace(work_of_art, short_substr)
          while True:
              tokens = sentence.split()
              found_substr = False
              for i in range(len(tokens) - 2):
                  found = True
                  for j in range(i, i + 3):
                      if (
                          len(tokens[j]) < 2
                          or tokens[j][0] in '("'
                          or tokens[j][-1] in '"),.?'
                      ):
                          found = False
                  if found and i > 0:
                      token_tags = [
                          self.nlp(tokens[j])[0].pos_
                          for j in range(i, i + 3)
                      ]
                      lemm_tokens = {
                          self.nlp(tok)[0].lemma_
                          for tok in tokens[i : i + 3]
                      }
                      if (
                          token_tags == ["DET", "DET", "NOUN"]
                          and not lemm_tokens & self.first_tokens
                      ):
                          long_substr = " ".join(tokens[i : i + 3])
                          replace_dict[tokens[i + 2]] = (
                              long_substr,
                              "adj",
                          )
                          sentence = sentence.replace(
                              long_substr, tokens[i + 2]
                          )
                          found_substr = True
                  if found_substr:
                      break
              if not found_substr:
                  break
          sentence_tokens = [tok.text for tok in tokenize(sentence)]
          sentences_tokens_batch.append(sentence_tokens)
          log.debug(
              f"replace_dict: {replace_dict} --- sentence: {sentence_tokens}"
          )
          replace_dict_batch.append(replace_dict)
      return sentences_tokens_batch, replace_dict_batch
  
imports: import copy
  import re
  from collections import defaultdict
  from io import StringIO
  from logging import getLogger
  from typing import Any, List, Tuple, Dict, Union
  import spacy
  from navec import Navec
  from razdel import tokenize
  from slovnet import Syntax
  from udapi.block.read.conllu import Conllu
  from udapi.core.node import Node
  from deeppavlov.core.commands.utils import expand_path
  from deeppavlov.core.common.file import read_json
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
  from deeppavlov.core.models.serializable import Serializable
  from deeppavlov.models.kbqa.ru_adj_to_noun import RuAdjToNoun
  from deeppavlov.models.kbqa.utils import preprocess_template_queries
label_desc: Use image node_id 4 for calling the SlovnetSyntaxParser obj's underlying member method code with example usage: obj.preprocess_sentences(sentences, entity_offsets_batch) and returns: sentences_tokens_batch,  replace_dict_batch