code: def test_create_supervised_tbptt_trainer_callcounts(
      mock_detach_hidden,
  ):
      # Mocking objects
      model = mock.MagicMock()
      # Necessary to unpack output
      model.return_value = (1, 1)
      optimizer = mock.MagicMock()
      loss = mock.MagicMock()
  
      trainer = create_supervised_tbptt_trainer(
          model, optimizer, loss, tbtt_step=2
      )
  
      # Adding two mock handles to the trainer to monitor that TBPTT events are
      # called correctly
      handle_started = mock.MagicMock()
      trainer.add_event_handler(
          Tbptt_Events.TIME_ITERATION_STARTED, handle_started
      )
      handle_completed = mock.MagicMock()
      trainer.add_event_handler(
          Tbptt_Events.TIME_ITERATION_COMPLETED, handle_completed
      )
  
      # Fake data
      X = torch.ones(6, 2, 1)
      y = torch.ones(6, 2, 1)
      data = [(X, y)]
  
      # Running trainer
      trainer.run(data)
  
      # Verifications
      assert handle_started.call_count == 3
      assert handle_completed.call_count == 3
      assert mock_detach_hidden.call_count == 2
      assert model.call_count == 3
      assert loss.call_count == 3
      assert optimizer.zero_grad.call_count == 3
      assert optimizer.step.call_count == 3
      n_args_tuple = tuple(
          len(args) for args, kwargs in model.call_args_list
      )
      assert n_args_tuple == (1, 2, 2)
  
file_path: ignite/tests/ignite/contrib/engines/test_tbptt.py
imports: import unittest.mock
  import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from ignite.contrib.engines import create_supervised_tbptt_trainer, Tbptt_Events
  from ignite.contrib.engines.tbptt import _detach_hidden
label_desc: Use image node_id 4 for calling a global function with example usage: test_create_supervised_tbptt_trainer_callcounts(mock_detach_hidden) without return types