class_docstr: This sampler samples elements according to the Sampled Gaussian Mechanism.
  Each sample is selected with a probability equal to ``sample_rate``.
  The sampler generates ``steps`` number of batches, that defaults to 1/``sample_rate``.
class_docstr_tok: ["This","sampler","samples","elements","according","to","the","Sampled","Gaussian","Mechanism",".","Each","sample","is","selected","with","a","probability","equal","to","``","sample_rate","``",".","The","sampler","generates","``","steps","``","number","of","batches",",","that","defaults","to","1\/","``","sample_rate","``","."]
code: def __len__(self):
      return self.steps
  
file_path: opacus/opacus/utils/uniform_sampler.py
imports: from typing import List
  import torch
  from torch.utils.data import Sampler
label_desc: Use image node_id 2 for calling the UniformWithReplacementSampler obj's underlying member method code with example usage: obj.__len__() and returns: self