code: def get_train_test_datasets(path):
      # - Get train/test datasets
      if idist.get_rank() > 0:
          # Ensure that only rank 0 download the dataset
          idist.barrier()
  
      train_ds = datasets.CIFAR10(
          root=path,
          train=True,
          download=True,
          transform=train_transform,
      )
      test_ds = datasets.CIFAR10(
          root=path,
          train=False,
          download=False,
          transform=test_transform,
      )
  
      if idist.get_rank() == 0:
          # Ensure that only rank 0 download the dataset
          idist.barrier()
  
      return train_ds, test_ds
  
file_path: ignite/assets/tldr/teaser.py
imports: import os
  from pathlib import Path
  import torch
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import StepLR
  from torchvision import datasets, models
  from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
  import ignite.distributed
  from ignite.contrib.engines import common
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import Engine, Events, create_supervised_evaluator
  from ignite.metrics import Accuracy
label_desc: Use image node_id 1 for calling a global function with example usage: get_train_test_datasets(path) and returns: train_ds,  test_ds