text: from typing import TYPE_CHECKING, Optional, Callable
  from allennlp.training.callbacks.callback import Callback, handle_event
  from allennlp.training.callbacks.events import Events
  import logging
  logger = logging.getLogger(__name__)
  
  if TYPE_CHECKING:
      from allennlp.training.callback_trainer import CallbackTrainer
  
  
  @Callback.register("log_metrics_to_wandb")
  class LogMetricsToWandb(Callback):
      def __init__(self, debug=False, debug_log_freq: int = 100) -> None:
          # import wandb here to be sure that it was initialized
          # before this line was executed
          super().__init__()
          import wandb  # type: ignore
          self.wandb = wandb
          self.debug = debug
          self.debug_log_freq = debug_log_freq
          self._get_batch_num_total: Optional[Callable[[], int]] = None
  
      @handle_event(Events.TRAINING_START)
      def training_start(self, trainer: "CallbackTrainer") -> None:
          # This is an ugly hack to get the tensorboard instance to know about the trainer, because
          # the callbacks are defined before the trainer.
          self._get_batch_num_total = lambda: trainer.batch_num_total
  
      @handle_event(Events.EPOCH_END, priority=150)  # type: ignore
      def end_of_epoch_log(self, trainer: "CallbackTrainer") -> None:
          """ This should run after all the epoch end metrics have
          been computed by the metric_tracker callback.
  
          The final callback in metric_tracker for EPOCH_END event
          has priority 100. So this callback should have priority > 100
          to be executed after
          """
          logger.info("Writing metrics to wandb")
          self.wandb.log(trainer.metrics)
  
      @handle_event(Events.BATCH_END, priority=1000)  # type: ignore
      def end_of_batch_log(self, trainer: "CallbackTrainer") -> None:
          """Log metrics after each mini batch if in debug mode
  
          The final callback in metric_tracker for EPOCH_END event
          has priority 100. So this callback should have priority > 100
          to be executed after
          """
  
          if self.debug:
              if self._get_batch_num_total() % self.debug_log_freq == 0:
                  logger.info("Writing metrics to wandb")
                  self.wandb.log(trainer.metrics)
  
      @handle_event(Events.ERROR, priority=150)
      def log_after_crash(self, trainer: "CallbackTrainer") -> None:
          """ We would have tried validation after nan. Log current metrics anyway """
  
          if isinstance(trainer.exception, ValueError):
              if str(trainer.exception).strip() == "nan loss encountered":
                  logger.info(
                      "Nan loss encountered. Logging current metrics to wandb")
                  self.wandb.log(trainer.metrics)
  