code: def parse_data(
      self, data: PathLike or PosixPath, out_dir: Union[str, PathLike]
  ):
      if out_dir:
          Path(out_dir).mkdir(parents=True, exist_ok=True)
      if self.parser_name == "bist":
          _, data_size = parse_docs_bist(
              self.parser, data, out_dir=out_dir
          )
      elif self.parser_name == "spacy":
          parsed_docs = parse_docs(self.parser, data, out_dir=out_dir)
          data_size = sum(len(doc.sentences) for doc in parsed_docs)
      if data_size < 1000:
          raise ValueError(
              "The data contains only {0} sentences. A minimum of 1000 "
              "sentences is required for training.".format(data_size)
          )
      return out_dir
  
file_path: nlp-architect/nlp_architect/models/absa/train/train.py
imports: from pathlib import Path, PosixPath
  from os import PathLike
  from typing import Union
  from nlp_architect.models.absa import TRAIN_OUT, LEXICONS_OUT
  from nlp_architect.models.absa.train.acquire_terms import AcquireTerms
  from nlp_architect.models.absa.train.rerank_terms import RerankTerms
  from nlp_architect.models.absa.utils import parse_docs, parse_docs_bist, _download_pretrained_rerank_model, _write_aspect_lex, _write_opinion_lex
  from nlp_architect.utils.io import download_unzip
label_desc: Use image node_id 3 for calling the TrainSentiment obj's underlying member method code with example usage: obj.parse_data(data, out_dir) and returns: out_dir