class_docstr: Sampler that restricts data loading to a subset of the dataset for distributed,
  with repeated augmentation.
  It ensures that different each augmented version of a sample will be visible to a
  different process (GPU).
  Heavily based on 'torch.utils.data.DistributedSampler'.
  
  This is borrowed from the DeiT Repo:
  https://github.com/facebookresearch/deit/blob/main/samplers.py
code: def set_epoch(self, epoch):
      self.epoch = epoch
  
file_path: vision/references/classification/sampler.py
imports: import math
  import torch
  import torch.distributed
label_desc: Use image node_id 4 for calling the RASampler obj's underlying member method code with example usage: obj.set_epoch(epoch) without return types