code: def test_saved_content(self):
      with tempfile.TemporaryDirectory() as tempdir:
          data = [
              {
                  "pred": torch.zeros(8),
                  PostFix.meta("image"): {
                      "filename_or_obj": [
                          "testfile" + str(i) for i in range(8)
                      ]
                  },
              },
              {
                  "pred": torch.zeros(8),
                  PostFix.meta("image"): {
                      "filename_or_obj": [
                          "testfile" + str(i) for i in range(8, 16)
                      ]
                  },
              },
              {
                  "pred": torch.zeros(8),
                  PostFix.meta("image"): {
                      "filename_or_obj": [
                          "testfile" + str(i) for i in range(16, 24)
                      ]
                  },
              },
          ]
  
          saver = CSVSaver(
              output_dir=Path(tempdir),
              filename="predictions2.csv",
              overwrite=False,
              flush=False,
              delimiter="\t",
          )
          # set up test transforms
          post_trans = Compose(
              [
                  CopyItemsd(
                      keys=PostFix.meta("image"),
                      times=1,
                      names=PostFix.meta("pred"),
                  ),
                  # 1st saver saves data into CSV file
                  SaveClassificationd(
                      keys="pred",
                      saver=None,
                      meta_keys=None,
                      output_dir=Path(tempdir),
                      filename="predictions1.csv",
                      delimiter="\t",
                      overwrite=True,
                  ),
                  # 2rd saver only saves data into the cache, manually finalize later
                  SaveClassificationd(
                      keys="pred",
                      saver=saver,
                      meta_key_postfix=PostFix.meta(),
                  ),
              ]
          )
          # simulate inference 2 iterations
          d = decollate_batch(data[0])
          for i in d:
              post_trans(i)
          d = decollate_batch(data[1])
          for i in d:
              post_trans(i)
          # write into CSV file
          saver.finalize()
  
          # 3rd saver will not delete previous data due to `overwrite=False`
          trans2 = SaveClassificationd(
              keys="pred",
              saver=None,
              meta_keys=PostFix.meta(
                  "image"
              ),  # specify meta key, so no need to copy anymore
              output_dir=tempdir,
              filename="predictions1.csv",
              delimiter="\t",
              overwrite=False,
          )
          d = decollate_batch(data[2])
          for i in d:
              trans2(i)
  
          def _test_file(filename, count):
              filepath = os.path.join(tempdir, filename)
              self.assertTrue(os.path.exists(filepath))
              with open(filepath) as f:
                  reader = csv.reader(f, delimiter="\t")
                  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, count)
  
          _test_file("predictions1.csv", 24)
          _test_file("predictions2.csv", 16)
  
file_path: MONAI/tests/test_save_classificationd.py
imports: from __future__ import annotations
  import csv
  import os
  import tempfile
  import unittest
  from pathlib import Path
  import numpy
  import torch
  from monai.data import CSVSaver, decollate_batch
  from monai.transforms import Compose, CopyItemsd, SaveClassificationd
  from monai.utils.enums import PostFix
label_desc: Use image node_id 1 for calling the TestSaveClassificationd obj's underlying member method code with example usage: obj.test_saved_content() without return types