code: def cifar_trainset(local_rank, dl_path="/tmp/cifar10-data"):
      transform = transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
  
      # Ensure only one rank downloads.
      # Note: if the download path is not on a shared filesytem, remove the semaphore
      # and switch to args.local_rank
      dist.barrier()
      if local_rank != 0:
          dist.barrier()
      trainset = torchvision.datasets.CIFAR10(
          root=dl_path, train=True, download=True, transform=transform
      )
      if local_rank == 0:
          dist.barrier()
      return trainset
  
file_path: deepspeedexamples/training/pipeline_parallelism/train.py
imports: import os
  import argparse
  import torch
  import torch.distributed
  import torchvision
  import torchvision.transforms
  from torchvision.models import AlexNet
  from torchvision.models import vgg19
  import deepspeed
  from deepspeed.pipe import PipelineModule
  from deepspeed.utils import RepeatingLoader
label_desc: Use image node_id 1 for calling a global function with example usage: cifar_trainset(local_rank, dl_path) and returns: trainset