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 __len__(self):
      return self.num_selected_samples
  
file_path: vision/references/classification/sampler.py
imports: import math
  import torch
  import torch.distributed
label_desc: Use image node_id 3 for calling the RASampler obj's underlying member method code with example usage: obj.__len__() and returns: self