code: def test_tuplestoweights_sampler(self):
      model = models.resnet18(pretrained=True)
      model.fc = torch.nn.Identity()
      model = torch.nn.DataParallel(model)
      model.to(TEST_DEVICE)
  
      miner = MultiSimilarityMiner(epsilon=-0.2)
  
      eval_transform = transforms.Compose(
          [
              transforms.Resize(128),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
  
      temporary_folder = (
          "cifar100_temp_for_pytorch_metric_learning_test"
      )
  
      assert not os.path.isdir(temporary_folder)
  
      dataset = datasets.CIFAR100(
          temporary_folder,
          train=True,
          download=True,
          transform=eval_transform,
      )
      subset_size = 1000
      sampler = TuplesToWeightsSampler(
          model, miner, dataset, subset_size=subset_size
      )
      iterable_as_list = list(iter(sampler))
      self.assertTrue(len(iterable_as_list) == subset_size)
      unique_idx = torch.unique(torch.tensor(iterable_as_list))
      self.assertTrue(torch.all(sampler.weights[unique_idx] != 0))
  
      shutil.rmtree(temporary_folder)
  
file_path: pytorch-metric-learning/tests/samplers/test_tuples_to_weights_sampler.py
imports: import os
  import shutil
  import unittest
  import torch
  from torchvision import datasets, models, transforms
  from pytorch_metric_learning.miners import MultiSimilarityMiner
  from pytorch_metric_learning.samplers import TuplesToWeightsSampler
  from ..None import TEST_DEVICE
label_desc: Use image node_id 1 for calling the TestTuplesToWeightsSampler obj's underlying member method code with example usage: obj.test_tuplestoweights_sampler() without return types