class_docstr: 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, cands, other_preds):
      """Ensemble the dict with statistical model predictions."""
      expansions = []
      assert len(cands) == len(other_preds)
      for c, pred in zip(cands, other_preds):
          if c in self.expansion_dict:
              expansions += [self.expansion_dict[c]]
          elif c.lower() in self.expansion_dict:
              expansions += [self.expansion_dict[c.lower()]]
          else:
              expansions += [pred]
      return expansions
  
imports: 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.trainer import Trainer
  from stanza.models.common.seq2seq_model import Seq2SeqModel
  from stanza.models.common import utils, loss
  from stanza.models.mwt.vocab import Vocab
label_desc: Use image node_id 6 for calling the Trainer obj's underlying member method code with example usage: obj.ensemble(cands, other_preds) and returns: expansions