code: def parse_features(line, n_feats=0, defaults=None):
      """
      Parses text lines with features appended to each token.
      Ex.: This￨A￨B is￨A￨A a￨C￨A test￨A￨B
      """
      text, feats = [], [[] for _ in range(n_feats)]
      check, count = 0, 0
      for token in line.split(" "):
          tok, *fts = token.strip().split("￨")
          check += len(fts)
          count += 1
          if not fts and defaults is not None:
              if isinstance(defaults, str):
                  defaults = defaults.split("￨")
              if n_feats > 0:
                  assert len(defaults) == n_feats  # Security check
                  fts = defaults
          assert len(fts) == n_feats, (
              f"The number of fetures does not match the "
              f"expected number of features. Found {len(fts)} "
              f"features in the data but {n_feats} were expected."
          )
          text.append(tok)
          for i in range(n_feats):
              feats[i].append(fts[i])
      # Check if all tokens have features or none at all
      assert (
          check == 0 or check == count * n_feats
      ), "Some tokens are missing features. Please check your data."
      feats = [" ".join(x) for x in feats] if n_feats > 0 else None
      return " ".join(text), feats
  
file_path: OpenNMT-py/onmt/inputters/text_utils.py
imports: import torch
  import pyonmttok
  from onmt.constants import DefaultTokens, CorpusTask, ModelTask
  from torch.nn.utils.rnn import pad_sequence
  from onmt.utils.logging import logger
  from collections import Counter
label_desc: Use image node_id 1 for calling a global function with example usage: parse_features(line, n_feats, defaults) and returns: str,  feats