code: def test_pca(self):
      # just make sure pca runs without crashing
      model = torch.nn.Identity()
      AC = accuracy_calculator.AccuracyCalculator(
          include=("precision_at_1",)
      )
      embeddings = torch.randn(1024, 512)
      labels = torch.randint(0, 10, size=(1024,))
      dataset_dict = {"train": c_f.EmbeddingDataset(embeddings, labels)}
      pca_size = 16
  
      def end_of_testing_hook(tester):
          self.assertTrue(
              tester.embeddings_and_labels["train"][0].shape[1]
              == pca_size
          )
  
      tester = GlobalEmbeddingSpaceTester(
          pca=pca_size,
          accuracy_calculator=AC,
          end_of_testing_hook=end_of_testing_hook,
      )
      tester.test(dataset_dict, 0, model)
      self.assertTrue(not hasattr(tester, "embeddings_and_labels"))
  
file_path: pytorch-metric-learning/tests/testers/test_global_embedding_space_tester.py
imports: import unittest
  import torch
  from pytorch_metric_learning.testers import GlobalEmbeddingSpaceTester
  from pytorch_metric_learning.utils import accuracy_calculator
  from pytorch_metric_learning.utils import common_functions
  from ..zzz_testing_utils.testing_utils import angle_to_coord
label_desc: Use image node_id 3 for calling the TestGlobalEmbeddingSpaceTester obj's underlying member method code with example usage: obj.test_pca() without return types