code: def test_length_weighted_random_sampler(
      self,
  ):  # pylint: disable=no-self-use
      for _ in range(1000):
          # gerenate a lenght unbalanced dataset with random max/min audio lenght
          min_audio = random.randrange(1, 22050)
          max_audio = random.randrange(44100, 220500)
          for idx, item in enumerate(train_samples):
              # increase the diversity of durations
              random_increase = random.randrange(100, 1000)
              if idx < 5:
                  item["audio_length"] = min_audio + random_increase
              else:
                  item["audio_length"] = max_audio + random_increase
  
          weighted_sampler = (
              torch.utils.data.sampler.WeightedRandomSampler(
                  get_length_balancer_weights(
                      train_samples, num_buckets=2
                  ),
                  len(train_samples),
              )
          )
          ids = functools.reduce(
              lambda a, b: a + b,
              [list(weighted_sampler) for i in range(100)],
          )
          len1, len2 = 0, 0
          for index in ids:
              if train_samples[index]["audio_length"] < max_audio:
                  len1 += 1
              else:
                  len2 += 1
          assert is_balanced(
              len1, len2
          ), "Length Weighted sampler is supposed to be balanced"
  
file_path: TTS/tests/data_tests/test_samplers.py
imports: import functools
  import random
  import unittest
  import torch
  from TTS.config.shared_configs import BaseDatasetConfig
  from TTS.tts.datasets import load_tts_samples
  from TTS.tts.utils.data import get_length_balancer_weights
  from TTS.tts.utils.languages import get_language_balancer_weights
  from TTS.tts.utils.speakers import get_speaker_balancer_weights
  from TTS.utils.samplers import BucketBatchSampler, PerfectBatchSampler
label_desc: Use image node_id 6 for calling the TestSamplers obj's underlying member method code with example usage: obj.test_length_weighted_random_sampler() without return types