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 sampler was taken from https://github.com/facebookresearch/deit/blob/0c4b8f60/samplers.py
  Used in
  Copyright (c) 2015-present, Facebook, Inc.
code: def __len__(self):
      return self.num_selected_samples
  
file_path: pytorch-image-models/timm/data/distributed_sampler.py
imports: import math
  import torch
  from torch.utils.data import Sampler
  import torch.distributed
label_desc: Use image node_id 3 for calling the RepeatAugSampler obj's underlying member method code with example usage: obj.__len__() and returns: self