code: def validation_step(
      self, batch: Tuple[Tensor, Tensor, List[str]], batch_idx: int
  ) -> Tensor:
      images, targets = batch[0], batch[1]
      features = self.forward(images).flatten(start_dim=1)
      cls_loss, cls_log = self.online_classifier.validation_step(
          (features.detach(), targets), batch_idx
      )
      self.log_dict(
          cls_log,
          prog_bar=True,
          sync_dist=True,
          batch_size=len(targets),
      )
      return cls_loss
  
imports: import copy
  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.tico_loss import TiCoLoss
  from lightly.models.modules.heads import TiCoProjectionHead
  from lightly.models.utils import deactivate_requires_grad, get_weight_decay_parameters, update_momentum
  from lightly.transforms import BYOLTransform
  from lightly.utils.benchmarking import OnlineLinearClassifier
  from lightly.utils.lars import LARS
  from lightly.utils.scheduler import CosineWarmupScheduler, cosine_schedule
label_desc: Use image node_id 5 for calling the TiCo obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: cls_loss