class_docstr: A trainer for training models. 
class_docstr_tok: ["A","trainer","for","training","models","."]
docstr: Predict a list of lemmas using the dict model given (word, pos) pairs. 
docstr_tok: ["Predict","a","list","of","lemmas","using","the","dict","model","given","(","word",",","pos",")","pairs","."]
code: def predict_dict(self, pairs):
      """Predict a list of lemmas using the dict model given (word, pos) pairs."""
      lemmas = []
      for p in pairs:
          w, pos = p
          if (w, pos) in self.composite_dict:
              lemmas += [self.composite_dict[(w, pos)]]
          elif w in self.word_dict:
              lemmas += [self.word_dict[w]]
          else:
              lemmas += [w]
      return lemmas
  
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 8 for calling the Trainer obj's underlying member method code with example usage: obj.predict_dict(pairs) and returns: lemmas