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 __iter__(self):
      indices = []
      for _ in range(self.num_samples):
          idx = np.random.choice(self.idx_classes)
          indice = int(np.random.choice(self.indices[idx]))
          indices.append(indice)
      return iter(indices)
  
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 2 for calling the BalancedRandomSampler obj's underlying member method code with example usage: obj.__iter__() and returns: iter