def _validation_loss(self) -> Tuple[float, int]:
        """
        Computes the validation loss. Returns it and the number of batches.
        """
        logger.info("Validating")

        self.model.eval()

        # Replace parameter values with the shadow values from the moving averages.
        if self._moving_average is not None:
            self._moving_average.assign_average_value()

        if self._validation_iterator is not None:
            val_iterator = self._validation_iterator
        else:
            val_iterator = self.iterator

        num_gpus = len(self._cuda_devices)

        raw_val_generator = val_iterator(self._validation_data, num_epochs=1, shuffle=False)
        val_generator = lazy_groups_of(raw_val_generator, num_gpus)
        num_validation_batches = math.ceil(
            val_iterator.get_num_batches(self._validation_data) / num_gpus
        )
        val_generator_tqdm = Tqdm.tqdm(val_generator, total=num_validation_batches)
        batches_this_epoch = 0
        val_loss = 0
        for batch_group in val_generator_tqdm:

            loss = self.batch_loss(batch_group, for_training=False)
            if loss is not None:
                # You shouldn't necessarily have to compute a loss for validation, so we allow for
                # `loss` to be None.  We need to be careful, though - `batches_this_epoch` is
                # currently only used as the divisor for the loss function, so we can safely only
                # count those batches for which we actually have a loss.  If this variable ever
                # gets used for something else, we might need to change things around a bit.
                batches_this_epoch += 1
                val_loss += loss.detach().cpu().numpy()

            # Update the description with the latest metrics
            val_metrics = training_util.get_metrics(self.model, val_loss, batches_this_epoch)
            description = training_util.description_from_metrics(val_metrics)
            val_generator_tqdm.set_description(description, refresh=False)

        # Now restore the original parameter values.
        if self._moving_average is not None:
            self._moving_average.restore()

        return val_loss, batches_this_epoch

        Computes the validation loss. Returns it and the number of batches.
        
gector/trainer.py