code: def _get_dist_cifar10(args):
      train_transform, valid_transform = _data_transforms_cifar10(args)
      train_data = dset.CIFAR10(
          root=args.data,
          train=True,
          download=True,
          transform=train_transform,
      )
      valid_data = dset.CIFAR10(
          root=args.data,
          train=False,
          download=True,
          transform=valid_transform,
      )
  
      sampler = torch.utils.data.distributed.DistributedSampler(
          train_data, num_replicas=args.gpu_num, rank=args.local_rank
      )
  
      train_queue = torch.utils.data.DataLoader(
          train_data,
          batch_size=args.batch_size // args.gpu_num,
          pin_memory=True,
          num_workers=4,
          drop_last=True,
          sampler=sampler,
      )
  
      valid_queue = torch.utils.data.DataLoader(
          valid_data,
          batch_size=args.batch_size,
          shuffle=False,
          pin_memory=True,
          num_workers=4,
      )
      return train_queue, valid_queue, sampler
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 6 for calling a global function with example usage: _get_dist_cifar10(args) and returns: train_queue,  valid_queue,  sampler