def test_log_dir_custom_logger(tmp_path):
    """Tests that the path is correct even when there is a custom logger."""
    expected = str(tmp_path / "custom_logs" / "version_0")
    model = TestModel(expected)

    trainer = Trainer(
        default_root_dir=tmp_path,
        max_steps=2,
        callbacks=[ModelCheckpoint(dirpath=tmp_path)],
        logger=TensorBoardLogger(save_dir=tmp_path, name="custom_logs"),
    )

    assert trainer.log_dir == expected
    trainer.fit(model)
    assert trainer.log_dir == expected
Tests that the path is correct even when there is a custom logger.
tests/tests_pytorch/trainer/properties/test_log_dir.py