code: def test_simple(self):
      num_classes = 10
      num_samples = 10000
  
      p = np.asarray([2**i for i in range(num_classes)]).astype(float)
      p /= p.sum()
  
      labels = np.random.choice(range(num_classes), num_samples, p=p)
  
      sampler = BalancedRandomSampler(labels)
  
      indices = iter(sampler)
      _, c = np.unique(labels[list(indices)], return_counts=True)
      self.assertGreater(0.005, np.std(c) / num_samples)
  
imports: import os
  import sys
  import unittest
  import numpy
  from torch_points3d.datasets.samplers import BalancedRandomSampler
label_desc: Use image node_id 1 for calling the TestBalancedRandomSampler obj's underlying member method code with example usage: obj.test_simple() without return types