code: def _test_create_mocked_supervised_trainer(
      model_device: Optional[str] = None,
      trainer_device: Optional[str] = None,
      trace: bool = False,
      amp_mode: str = None,
      scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False,
  ):
      with mock.patch(
          "ignite.engine.supervised_training_step_amp"
      ) as training_step_amp_mock:
          with mock.patch(
              "ignite.engine.supervised_training_step_apex"
          ) as training_step_apex_mock:
              with mock.patch(
                  "ignite.engine.supervised_training_step_tpu"
              ) as training_step_tpu_mock:
                  with mock.patch(
                      "ignite.engine.supervised_training_step"
                  ) as training_step_mock:
                      trainer, _ = _default_create_supervised_trainer(
                          model_device=model_device,
                          trainer_device=trainer_device,
                          trace=trace,
                          amp_mode=amp_mode,
                          scaler=scaler,
                      )
  
                      x = torch.tensor([[0.1], [0.2]])
                      y = torch.tensor([[0.3], [0.5]])
                      data = [(x, y)]
  
                      on_tpu = (
                          "xla" in trainer_device
                          if trainer_device is not None
                          else False
                      )
                      on_mps = (
                          "mps" in trainer_device
                          if trainer_device is not None
                          else False
                      )
                      mode, _ = _check_arg(
                          on_tpu, on_mps, amp_mode, scaler
                      )
  
                      if model_device == trainer_device or (
                          (model_device == "cpu")
                          ^ (trainer_device == "cpu")
                      ):
                          trainer.run(data)
  
                          if mode == "amp":
                              assert training_step_amp_mock.called
                          elif mode == "apex":
                              assert training_step_apex_mock.called
                          elif mode == "tpu":
                              assert training_step_tpu_mock.called
                          else:
                              assert training_step_mock.called
  
imports: import os
  from importlib.util import find_spec
  from typing import Optional, Union
  from unittest import mock
  from unittest.mock import MagicMock, patch
  import pytest
  import torch
  from packaging.version import Version
  from pytest import approx
  from torch.nn.functional import mse_loss
  from torch.optim import SGD
  import ignite.distributed
  from ignite.distributed.comp_models.base import _torch_version_le_112
  from ignite.engine import _check_arg, create_supervised_evaluator, create_supervised_trainer, Engine, Events, supervised_evaluation_step, supervised_evaluation_step_amp, supervised_training_step_tpu
  from ignite.metrics import MeanSquaredError
label_desc: Use image node_id 4 for calling a global function with example usage: _test_create_mocked_supervised_trainer(model_device, trainer_device, trace, amp_mode, scaler) without return types