class_docstr: Main class for sentiment inference execution.
  
  Attributes:
      opinion_lex: Opinion lexicon as outputted by TrainSentiment module.
      aspect_lex: Aspect lexicon as outputted by TrainSentiment module.
      intensifier_lex (dict): Pre-defined intensifier lexicon.
      negation_lex (dict): Pre-defined negation lexicon.
class_docstr_tok: ["Main","class","for","sentiment","inference","execution",".","Attributes",":","opinion_lex",":","Opinion","lexicon","as","outputted","by","TrainSentiment","module",".","aspect_lex",":","Aspect","lexicon","as","outputted","by","TrainSentiment","module",".","intensifier_lex","(","dict",")",":","Pre-defined","intensifier","lexicon",".","negation_lex","(","dict",")",":","Pre-defined","negation","lexicon","."]
docstr: Extract intensifier events from sentence.
docstr_tok: ["Extract","intensifier","events","from","sentence","."]
code: def _extract_intensifier_terms(
      self, toks, sentiment_index, polarity, sentence
  ):
      """Extract intensifier events from sentence."""
      count = 0
      terms = []
      for intens_i, intens in [
          (i, x)
          for i, x in enumerate(toks)
          if x in self.intensifier_lex
      ]:
          if math.fabs(sentiment_index - intens_i) == 1:
              score = self.intensifier_lex[intens].score
              terms.append(
                  Term(
                      intens,
                      TermType.INTENSIFIER,
                      polarity,
                      score,
                      sentence[intens_i]["start"],
                      sentence[intens_i]["len"],
                  )
              )
              count += abs(score + float(INTENSIFIER_FACTOR))
      return count if count != 0 else 1, terms
  
imports: import math
  from os import PathLike
  from pathlib import Path, PosixPath
  from typing import Union
  from nlp_architect.common.core_nlp_doc import CoreNLPDoc
  from nlp_architect.models.absa import INFERENCE_OUT
  from nlp_architect.models.absa.inference.data_types import Term, TermType, Polarity, SentimentDoc, SentimentSentence, LexiconElement
  from nlp_architect.models.absa.utils import _read_lexicon_from_csv, load_opinion_lex, _load_aspect_lexicon, parse_docs, parse_docs_bist, _load_parsed_docs_from_dir
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling the SentimentInference obj's underlying member method code with example usage: obj._extract_intensifier_terms(toks, sentiment_index, polarity, sentence) and returns: unknown,  terms