code: def test_specify_gpu(self):
      max_world_size = min(4, torch.cuda.device_count())
      for i in range(1, max_world_size + 1):
          for gpus in itertools.combinations(range(max_world_size), i):
              knn_func = FaissKNN(gpus=gpus)
              AC = accuracy_calculator.AccuracyCalculator(
                  knn_func=knn_func
              )
              embeddings = torch.randn(1000, 32)
              labels = torch.randint(0, 10, size=(1000,))
              AC.get_accuracy(embeddings, labels)
  
imports: import itertools
  import logging
  import unittest
  import numpy
  import torch
  from pytorch_metric_learning.distances import LpDistance
  from pytorch_metric_learning.utils import accuracy_calculator
  from pytorch_metric_learning.utils.common_functions import LOGGER
  from pytorch_metric_learning.utils.inference import CustomKNN, FaissKMeans, FaissKNN
  from ..None import TEST_DEVICE
label_desc: Use image node_id 1 for calling the TestCalculateAccuraciesFaissKNN obj's underlying member method code with example usage: obj.test_specify_gpu() without return types