class_docstr: A trainer for training models. 
class_docstr_tok: ["A","trainer","for","training","models","."]
code: def predict(self, batch, beam_size=1):
      device = next(self.model.parameters()).device
      inputs, orig_idx, text = unpack_batch(batch, device)
      src, src_mask, tgt, tgt_mask, pos, edits = inputs
  
      self.model.eval()
      batch_size = src.size(0)
      preds, edit_logits = self.model.predict(
          src, src_mask, pos=pos, beam_size=beam_size, raw=text
      )
      pred_seqs = [
          self.vocab["char"].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
      pred_tokens = utils.unsort(pred_tokens, orig_idx)
      if self.args.get("edit", False):
          assert edit_logits is not None
          edits = (
              np.argmax(edit_logits.data.cpu().numpy(), axis=1)
              .reshape([batch_size])
              .tolist()
          )
          edits = utils.unsort(edits, orig_idx)
      else:
          edits = None
      return pred_tokens, edits
  
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 4 for calling the Trainer obj's underlying member method code with example usage: obj.predict(batch, beam_size) and returns: pred_tokens,  edits