text: from __future__ import division
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data.sampler import Sampler
  
  class RandomSampler(Sampler):
  
      def __init__(self, data_source, checkpoint):
          self.data_source = data_source
          if checkpoint is not None and checkpoint['dataset_perm'] is not None:
              self.dataset_perm = checkpoint['dataset_perm']
              self.perm = self.dataset_perm[checkpoint['batch_size']*checkpoint['batch_idx']:]
          else:
              self.dataset_perm = torch.randperm(len(self.data_source)).tolist()
              self.perm = torch.randperm(len(self.data_source)).tolist() 
  
      def __iter__(self):
          return iter(self.perm)
      
      def __len__(self):
          return len(self.perm)
  
  class SequentialSampler(Sampler):
  
      def __init__(self, data_source, checkpoint):
          self.data_source = data_source
          if checkpoint is not None and checkpoint['dataset_perm'] is not None:
              self.dataset_perm = checkpoint['dataset_perm']
              self.perm = self.dataset_perm[checkpoint['batch_size']*checkpoint['batch_idx']:]
          else:
              self.dataset_perm = list(range(len(self.data_source)))
              self.perm = self.dataset_perm
  
      def __iter__(self):
          return iter(self.perm)
      
      def __len__(self):
          return len(self.perm)
  
  class CheckpointDataLoader(DataLoader):
      """
      Extends torch.utils.data.DataLoader to handle resuming training from an arbitrary point within an epoch.
      """
      def __init__(self, dataset, checkpoint=None, batch_size=1,
                   shuffle=False, num_workers=0, pin_memory=False, drop_last=True,
                   timeout=0, worker_init_fn=None):
  
          if shuffle:
              sampler = RandomSampler(dataset, checkpoint)
          else:
              sampler = SequentialSampler(dataset, checkpoint)
          if checkpoint is not None:
              self.checkpoint_batch_idx = checkpoint['batch_idx']
          else:
              self.checkpoint_batch_idx = 0
  
          super(CheckpointDataLoader, self).__init__(dataset, sampler=sampler, shuffle=False, batch_size=batch_size, num_workers=num_workers,
                                                     drop_last=drop_last, pin_memory=pin_memory, timeout=timeout, worker_init_fn=None)
  