class_docstr: This sampler is responsible for creating balanced batch based on the class distribution.
  It is implementing a replacement=True strategy for indices selection
code: def __len__(self):
      return self.num_samples
  
file_path: torch-points3d/torch_points3d/datasets/samplers.py
imports: import torch
  import numpy
  from torch.utils.data import Sampler
label_desc: Use image node_id 3 for calling the BalancedRandomSampler obj's underlying member method code with example usage: obj.__len__() and returns: self