code: def train_epoch(epoch, dataloader, model, loss_fn, optimizer):
      if ray.train.get_context().get_world_size() > 1:
          dataloader.sampler.set_epoch(epoch)
  
      size = (
          len(dataloader.dataset)
          // train.get_context().get_world_size()
      )
      model.train()
      for batch, (X, y) in enumerate(dataloader):
          # Compute prediction error
          pred = model(X)
          loss = loss_fn(pred, y)
  
          # Backpropagation
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          if batch % 100 == 0:
              loss, current = loss.item(), batch * len(X)
              print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
  
imports: import argparse
  import os
  import tempfile
  import torch
  import torch.nn
  import torchvision.transforms
  from filelock import FileLock
  from torch.utils.data import DataLoader, Subset
  from torchvision.datasets import CIFAR10
  from torchvision.models import resnet18
  import ray
  import ray.cloudpickle
  from ray import train, tune
  from ray.train import Checkpoint, FailureConfig, RunConfig, ScalingConfig
  from ray.train.torch import TorchTrainer
  from ray.tune.schedulers import PopulationBasedTraining
  from ray.tune.tune_config import TuneConfig
  from ray.tune.tuner import Tuner
label_desc: Use image node_id 1 for calling a global function with example usage: train_epoch(epoch, dataloader, model, loss_fn, optimizer) without return types