code: def test_saved_content(self):
      with tempfile.TemporaryDirectory() as tempdir:
          rank = dist.get_rank()
  
          # set up engine
          def _train_func(engine, batch):
              engine.state.batch = decollate_batch(batch)
              return [torch.zeros(1) for _ in range(8 + rank * 2)]
  
          engine = Engine(_train_func)
  
          # set up testing handler
          saver = ClassificationSaver(
              output_dir=tempdir,
              filename="predictions.csv",
              save_rank=1,
          )
          saver.attach(engine)
  
          # rank 0 has 8 images, rank 1 has 10 images
          data = [
              {
                  "filename_or_obj": [
                      "testfile" + str(i)
                      for i in range(8 * rank, (8 + rank) * (rank + 1))
                  ],
                  "data_shape": torch.ones((8 + rank * 2, 1, 1)),
              }
          ]
          # rank 1 has more iterations
          if rank == 1:
              data.append(
                  {
                      "filename_or_obj": [
                          "testfile" + str(i) for i in range(18, 28)
                      ],
                      "data_shape": torch.ones((10, 1, 1)),
                  }
              )
  
          engine.run(data, max_epochs=1)
          filepath = os.path.join(tempdir, "predictions.csv")
          if rank == 1:
              self.assertTrue(os.path.exists(filepath))
              with open(filepath) as f:
                  reader = csv.reader(f)
                  i = 0
                  for row in reader:
                      self.assertEqual(row[0], "testfile" + str(i))
                      self.assertEqual(
                          np.array(row[1:]).astype(np.float32), 0.0
                      )
                      i += 1
                  self.assertEqual(i, 28)
  
file_path: MONAI/tests/test_handler_classification_saver_dist.py
imports: from __future__ import annotations
  import csv
  import os
  import tempfile
  import unittest
  import numpy
  import torch
  import torch.distributed
  from ignite.engine import Engine
  from monai.data import decollate_batch
  from monai.handlers import ClassificationSaver
  from tests.utils import DistCall, DistTestCase
label_desc: Use image node_id 1 for calling the DistributedHandlerClassificationSaver obj's underlying member method code with example usage: obj.test_saved_content() and returns: unknown