class_docstr: A trainer for training models. 
class_docstr_tok: ["A","trainer","for","training","models","."]
docstr: Ensemble the dict with statistical model predictions. 
docstr_tok: ["Ensemble","the","dict","with","statistical","model","predictions","."]
code: def ensemble(self, pairs, other_preds):
      """Ensemble the dict with statistical model predictions."""
      lemmas = []
      assert len(pairs) == len(other_preds)
      for p, pred in zip(pairs, other_preds):
          w, pos = p
          if (w, pos) in self.composite_dict:
              lemma = self.composite_dict[(w, pos)]
          elif w in self.word_dict:
              lemma = self.word_dict[w]
          else:
              lemma = pred
          if lemma is None:
              lemma = w
          lemmas.append(lemma)
      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 10 for calling the Trainer obj's underlying member method code with example usage: obj.ensemble(pairs, other_preds) and returns: lemmas