docstr: Performs validation of data in batches
  
  :param val_batch: Batch data
  :param batch_idx: Batch indices
  
  :return: output - valid step loss
docstr_tok: ["Performs","validation","of","data","in","batches",":","param","val_batch",":","Batch","data",":","param","batch_idx",":","Batch","indices",":","return",":","output","-","valid","step","loss"]
code: def validation_step(self, val_batch, batch_idx):
      """
      Performs validation of data in batches
  
      :param val_batch: Batch data
      :param batch_idx: Batch indices
  
      :return: output - valid step loss
      """
      x, y = val_batch
      logits = self.forward(x)
      loss = self.cross_entropy_loss(logits, y)
      self.val_outputs.append(loss)
      return {"val_step_loss": loss}
  
imports: import os
  import lightning
  import torch
  from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint
  from lightning.pytorch.cli import LightningCLI
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
  from torchmetrics.functional import accuracy
  from torchvision import datasets, transforms
  import mlflow.pytorch
label_desc: Use image node_id 5 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.validation_step(val_batch, batch_idx) and returns: dict