class_docstr: This is an implementation of DANN for video data.
code: def training_step(self, batch, batch_nb):
      self._update_batch_epoch_factors(batch_nb)
  
      task_loss, adv_loss, log_metrics = self.compute_loss(
          batch, split_name="train"
      )
      if self.current_epoch < self._init_epochs:
          loss = task_loss
      else:
          loss = task_loss + self.lamb_da * adv_loss
  
      log_metrics["train_total_loss"] = loss
      log_metrics["train_adv_loss"] = adv_loss
      log_metrics["train_task_loss"] = task_loss
  
      self.log_dict(log_metrics, on_step=True, on_epoch=False)
  
      # logging alpha and lambda when they exist (they exist for DANN and CDAN but not for DAN and JAN)
      self.log(
          "alpha", self.alpha, on_step=False, on_epoch=True
      ) if hasattr(self, "alpha") else None
      self.log(
          "lambda", self.lamb_da, on_step=False, on_epoch=True
      ) if hasattr(self, "lamb_da") else None
  
      return {"loss": loss}
  
imports: import torch
  import kale.predict.losses
  from kale.loaddata.video_access import get_image_modality
  from kale.pipeline.domain_adapter import BaseMMDLike, CDANTrainer, DANNTrainer, GradReverse, Method, set_requires_grad, WDGRLTrainer
label_desc: Use image node_id 4 for calling the DANNTrainerVideo obj's underlying member method code with example usage: obj.training_step(batch, batch_nb) and returns: dict