code: def _maybe_log_save_evaluate(
      self, tr_loss, model, trial, epoch, ignore_keys_for_eval
  ):
      if self.control.should_log:
          logs: Dict[str, float] = {}
          tr_loss_scalar = tr_loss.item()
          # reset tr_loss to zero
          tr_loss -= tr_loss
  
          logs["loss"] = round(
              tr_loss_scalar
              / (self.state.global_step - self._globalstep_last_logged),
              4,
          )
          logs["learning_rate"] = self._get_learning_rate()
  
          self._total_loss_scalar += tr_loss_scalar
          self._globalstep_last_logged = self.state.global_step
  
          self.log(logs)
  
      if (
          self.args.start_eval_step > 0
          and self.state.global_step < self.args.start_eval_step
      ):
          return
  
      previous_best_metric = self.state.best_metric
      metrics = None
      if self.control.should_evaluate:
          metrics = self.evaluate()
          self._report_to_hp_search(trial, epoch, metrics)
  
      # Only save the checkpoint better than previous_best_metric
      if (
          self.args.save_better_checkpoint
          and self.args.metric_for_best_model is not None
      ):
          if metrics is not None and previous_best_metric is not None:
              if (
                  metrics[self.args.metric_for_best_model]
                  <= previous_best_metric
              ):
                  return
  
      if self.control.should_save:
          self._save_checkpoint(model, trial, metrics=metrics)
          self.control = self.callback_handler.on_save(
              self.args, self.state, self.control
          )
  
imports: import torch
  import torch.nn
  from dataclasses import dataclass, field
  from typing import Union, List, Dict, Tuple, Any, Optional
  from torch.cuda.amp import autocast
  from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
  from transformers.trainer_pt_utils import LabelSmoother
  from transformers.trainer import *
  from ..seq2seq.constraint_decoder import get_constraint_decoder
label_desc: Use image node_id 4 for calling the ConstraintSeq2SeqTrainer obj's underlying member method code with example usage: obj._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) without return types