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 __init__(self, labels, replacement=True):
      self.num_samples = len(labels)
  
      self.idx_classes, self.counts = np.unique(
          labels, return_counts=True
      )
      self.indices = {
          idx: np.argwhere(labels == idx).flatten()
          for idx in self.idx_classes
      }
  
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 1 to create a new BalancedRandomSampler object from inherited base classes: Sampler with example: obj = BalancedRandomSampler(labels, replacement)