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","."]
code: def parse_data(
      self,
      data: Union[PathLike, PosixPath],
      out_dir: Union[str, PathLike],
  ):
      if out_dir:
          Path(out_dir).mkdir(parents=True, exist_ok=True)
      parse_func = (
          parse_docs_bist if self.parser_name == "bist" else parse_docs
      )
      parse_func(self.parser, data, out_dir=out_dir)
      return out_dir
  
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 2 for calling the SentimentInference obj's underlying member method code with example usage: obj.parse_data(data, out_dir) and returns: out_dir