code: def validation_step(self, batch, batch_idx):
      x, y, _ = batch
      y_hat = self.forward(x)
      y_hat = torch.nn.functional.softmax(y_hat, dim=1)
  
      # calculate number of correct predictions
      _, predicted = torch.max(y_hat, 1)
      num = predicted.shape[0]
      correct = (predicted == y).float().sum()
      self.validation_step_outputs.append((num, correct))
      return num, correct
  
imports: import copy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchvision
  from lightly.data import LightlyDataset
  from lightly.loss import NTXentLoss
  from lightly.models import ResNetGenerator
  from lightly.models.modules.heads import MoCoProjectionHead
  from lightly.models.utils import batch_shuffle, batch_unshuffle, deactivate_requires_grad, update_momentum
  from lightly.transforms import MoCoV2Transform, utils
label_desc: Use image node_id 6 for calling the Classifier obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: num,  correct