code: def validate_epoch(
      dataloader, model, loss_fn, world_size: int, local_rank: int
  ):
      size = len(dataloader.dataset) // world_size
      num_batches = len(dataloader)
      model.eval()
      test_loss, correct = 0, 0
      with torch.no_grad():
          for X, y in dataloader:
              pred = model(X)
              test_loss += loss_fn(pred, y).item()
              correct += (
                  (pred.argmax(1) == y).type(torch.float).sum().item()
              )
      test_loss /= num_batches
      correct /= size
      print(
          f"[rank={local_rank}] Test Error: \n "
          f"Accuracy: {(100 * correct):>0.1f}%, "
          f"Avg loss: {test_loss:>8f} \n"
      )
      return test_loss
  
imports: import json
  import os
  import time
  from pathlib import Path
  from typing import Dict, Tuple
  import click
  import numpy
  import torch
  from torch import nn, distributed
  from torch.utils.data import DataLoader, DistributedSampler
  from torch.utils.data.dataloader import default_collate
  from torchvision import datasets
  from torchvision.transforms import ToTensor
label_desc: Use image node_id 3 for calling a global function with example usage: validate_epoch(dataloader, model, loss_fn, world_size, local_rank) and returns: test_loss