class_docstr: A trainer for training models. 
code: def predict(self, batch, unsort=True):
      device = next(self.model.parameters()).device
      inputs, orig_idx = unpack_batch(batch, device)
      src, src_mask, tgt, tgt_mask = inputs
  
      self.model.eval()
      batch_size = src.size(0)
      preds, _ = self.model.predict(
          src, src_mask, self.args["beam_size"]
      )
      pred_seqs = [
          self.vocab.unmap(ids) for ids in preds
      ]  # unmap to tokens
      pred_seqs = utils.prune_decoded_seqs(pred_seqs)
      pred_tokens = [
          "".join(seq) for seq in pred_seqs
      ]  # join chars to be tokens
      if unsort:
          pred_tokens = utils.unsort(pred_tokens, orig_idx)
      return pred_tokens
  
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 3 for calling the Trainer obj's underlying member method code with example usage: obj.predict(batch, unsort) and returns: pred_tokens