code: def create_sampler(datasets, shuffles, num_tasks, global_rank):
      samplers = []
      for dataset, shuffle in zip(datasets, shuffles):
          sampler = torch.utils.data.DistributedSampler(
              dataset,
              num_replicas=num_tasks,
              rank=global_rank,
              shuffle=shuffle,
          )
          samplers.append(sampler)
      return samplers
  
imports: import json
  import torch
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 2 for calling a global function with example usage: create_sampler(datasets, shuffles, num_tasks, global_rank) and returns: samplers