class_docstr: A trainer for training models. 
class_docstr_tok: ["A","trainer","for","training","models","."]
docstr: Determine if we can skip the seq2seq module when ensembling with the frequency lexicon. 
docstr_tok: ["Determine","if","we","can","skip","the","seq2seq","module","when","ensembling","with","the","frequency","lexicon","."]
code: def skip_seq2seq(self, pairs):
      """Determine if we can skip the seq2seq module when ensembling with the frequency lexicon."""
  
      skip = []
      for p in pairs:
          w, pos = p
          if (w, pos) in self.composite_dict:
              skip.append(True)
          elif w in self.word_dict:
              skip.append(True)
          else:
              skip.append(False)
      return skip
  
imports: import os
  import sys
  import numpy
  from collections import Counter
  import logging
  import torch
  from torch import nn
  import torch.nn.init
  import stanza.models.common.seq2seq_constant
  from stanza.models.common.foundation_cache import load_charlm
  from stanza.models.common.seq2seq_model import Seq2SeqModel
  from stanza.models.common.char_model import CharacterLanguageModelWordAdapter
  from stanza.models.common import utils, loss
  from stanza.models.lemma import edit
  from stanza.models.lemma.vocab import MultiVocab
label_desc: Use image node_id 9 for calling the Trainer obj's underlying member method code with example usage: obj.skip_seq2seq(pairs) and returns: skip