code: def validation_step(self, batch, batch_idx, dataloader_idx=0):
      input_nodes, output_nodes, mfgs = batch
      mfgs = [mfg.int().to(device) for mfg in mfgs]
      batch_inputs = mfgs[0].srcdata["features"]
      batch_labels = mfgs[-1].dstdata["labels"]
      batch_pred = self.module(mfgs, batch_inputs)
      loss = self.loss_fn(batch_pred, batch_labels)
      self.val_acc(batch_pred, batch_labels.int())
      self.log(
          "val_acc",
          self.val_acc,
          prog_bar=True,
          on_step=False,
          on_epoch=True,
          sync_dist=True,
          batch_size=batch_labels.shape[0],
      )
      self.log(
          "val_loss",
          loss,
          on_step=False,
          on_epoch=True,
          sync_dist=True,
          batch_size=batch_labels.shape[0],
      )
  
imports: import argparse
  import glob
  import math
  import os
  import time
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from ladies_sampler import LadiesSampler, normalized_edata, PoissonLadiesSampler
  from load_graph import load_dataset
  from model import SAGE
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer
  from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint
  from pytorch_lightning.loggers import TensorBoardLogger
  from torchmetrics.classification import MulticlassF1Score, MultilabelF1Score
label_desc: Use image node_id 6 for calling the SAGELightning obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx, dataloader_idx) without return types