code: def cifar_trainset(fp16=False):
      torchvision = pytest.importorskip(
          "torchvision", minversion="0.5.0"
      )
      import torchvision.transforms as transforms
  
      transform_list = [
          transforms.ToTensor(),
          transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
      ]
      if fp16:
          transform_list.append(
              torchvision.transforms.Lambda(cast_to_half)
          )
  
      transform = transforms.Compose(transform_list)
  
      local_rank = get_accelerator().current_device()
  
      # Only one rank per machine downloads.
      dist.barrier()
      if local_rank != 0:
          dist.barrier()
  
      data_root = os.getenv("TEST_DATA_DIR", "/tmp/")
      trainset = torchvision.datasets.CIFAR10(
          root=os.path.join(data_root, "cifar10-data"),
          train=True,
          download=True,
          transform=transform,
      )
      if local_rank == 0:
          dist.barrier()
      return trainset
  
imports: import pytest
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  import deepspeed
  import deepspeed.comm
  import deepspeed.runtime.utils
  from deepspeed.runtime.utils import required_torch_version
  from deepspeed.accelerator import get_accelerator
  from deepspeed.runtime.pipe.module import PipelineModule, LayerSpec
label_desc: Use image node_id 2 for calling a global function with example usage: cifar_trainset(fp16) and returns: trainset