code: def training_step(
      self,
      batch: Tuple[List[Tensor], Tensor, List[str]],
      batch_idx: int,
  ) -> Tensor:
      views, targets = batch[0], batch[1]
      features = self.forward(torch.cat(views)).flatten(start_dim=1)
      z = self.projection_head(features)
      z0, z1 = z.chunk(len(views))
      loss = self.criterion(z0, z1)
      self.log(
          "train_loss",
          loss,
          prog_bar=True,
          sync_dist=True,
          batch_size=len(targets),
      )
  
      cls_loss, cls_log = self.online_classifier.training_step(
          (features.detach(), targets.repeat(len(views))), batch_idx
      )
      self.log_dict(cls_log, sync_dist=True, batch_size=len(targets))
      return loss + cls_loss
  
imports: import math
  from typing import List, Tuple
  import torch
  from pytorch_lightning import LightningModule
  from torch import Tensor
  from torch.nn import Identity
  from torchvision.models import resnet50
  from lightly.loss.dcl_loss import DCLWLoss
  from lightly.models.modules import SimCLRProjectionHead
  from lightly.models.utils import get_weight_decay_parameters
  from lightly.transforms import SimCLRTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler
label_desc: Use image node_id 3 for calling the DCLW obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: unknown