code: def _create_internal_termbase(self, termbase_path):
      logger.debug(
          "Creating termbase with lemmas for Terminology transform"
      )
  
      # Use Spacy's stopwords to get rid of junk entries
      src_stopwords = self.src_nlp.Defaults.stop_words
      tgt_stopwords = self.tgt_nlp.Defaults.stop_words
      termbase = list()
      with open(termbase_path, mode="r", encoding="utf-8") as file:
          pairs = file.readlines()
          for pair in pairs:
              src_term, tgt_term = map(str, pair.split("\t"))
              src_lemma = " ".join(
                  "∥".join(tok.lemma_.split())
                  for tok in self.src_nlp(src_term)
              ).strip()
              tgt_lemma = " ".join(
                  tok.lemma_ for tok in self.tgt_nlp(tgt_term)
              ).strip()
              if (
                  src_lemma.lower() not in src_stopwords
                  and tgt_lemma.lower() not in tgt_stopwords
              ):
                  termbase.append((src_lemma, tgt_lemma))
      logger.debug(
          f"Created termbase with {len(termbase)} lemmas "
          f"for Terminology transform"
      )
      return termbase
  
imports: from onmt.utils.logging import logger
  from onmt.transforms import register_transform
  from .transform import Transform
  import spacy
  import ahocorasick
  import re
label_desc: Use image node_id 2 for calling the TermMatcher obj's underlying member method code with example usage: obj._create_internal_termbase(termbase_path) and returns: termbase