code: def test_after_train_iter(self):
      n = 50
      hook = CheckInvalidLossHook(n)
      runner = Mock()
      runner.logger = Mock()
      runner.logger.info = Mock()
  
      # Test `after_train_iter` function within the n iteration.
      runner.iter = 10
      outputs = dict(loss=torch.LongTensor([2]))
      hook.after_train_iter(runner, 10, outputs=outputs)
      outputs = dict(loss=torch.tensor(float("nan")))
      hook.after_train_iter(runner, 10, outputs=outputs)
      outputs = dict(loss=torch.tensor(float("inf")))
      hook.after_train_iter(runner, 10, outputs=outputs)
  
      # Test `after_train_iter` at the n iteration.
      runner.iter = n - 1
      outputs = dict(loss=torch.LongTensor([2]))
      hook.after_train_iter(runner, n - 1, outputs=outputs)
      outputs = dict(loss=torch.tensor(float("nan")))
      with self.assertRaises(AssertionError):
          hook.after_train_iter(runner, n - 1, outputs=outputs)
      outputs = dict(loss=torch.tensor(float("inf")))
      with self.assertRaises(AssertionError):
          hook.after_train_iter(runner, n - 1, outputs=outputs)
  
imports: from unittest import TestCase
  from unittest.mock import Mock
  import torch
  from mmdet.engine.hooks import CheckInvalidLossHook
label_desc: Use image node_id 1 for calling the TestCheckInvalidLossHook obj's underlying member method code with example usage: obj.test_after_train_iter() without return types