class_docstr: A trainer for training models. 
class_docstr_tok: ["A","trainer","for","training","models","."]
code: def predict(self, batch, unsort=True):
      device = next(self.model.parameters()).device
      (
          inputs,
          orig_idx,
          word_orig_idx,
          sentlens,
          wordlens,
          text,
      ) = unpack_batch(batch, device)
      (
          word,
          word_mask,
          wordchars,
          wordchars_mask,
          upos,
          xpos,
          ufeats,
          pretrained,
      ) = inputs
  
      self.model.eval()
      batch_size = word.size(0)
      _, preds = self.model(
          word,
          word_mask,
          wordchars,
          wordchars_mask,
          upos,
          xpos,
          ufeats,
          pretrained,
          word_orig_idx,
          sentlens,
          wordlens,
          text,
      )
      upos_seqs = [
          self.vocab["upos"].unmap(sent) for sent in preds[0].tolist()
      ]
      xpos_seqs = [
          self.vocab["xpos"].unmap(sent) for sent in preds[1].tolist()
      ]
      feats_seqs = [
          self.vocab["feats"].unmap(sent) for sent in preds[2].tolist()
      ]
  
      pred_tokens = [
          [
              [upos_seqs[i][j], xpos_seqs[i][j], feats_seqs[i][j]]
              for j in range(sentlens[i])
          ]
          for i in range(batch_size)
      ]
      if unsort:
          pred_tokens = utils.unsort(pred_tokens, orig_idx)
      return pred_tokens
  
file_path: stanza/stanza/models/pos/trainer.py
imports: import sys
  import logging
  import torch
  from torch import nn
  from stanza.models.common.trainer import Trainer
  from stanza.models.common import utils, loss
  from stanza.models.common.foundation_cache import NoTransformerFoundationCache
  from stanza.models.pos.model import Tagger
  from stanza.models.pos.vocab import MultiVocab
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