def test_trainer_reset_correctly(tmp_path):
    """Check that all trainer parameters are reset correctly after lr_find()"""
    model = BoringModel()
    model.lr = 0.1

    # logger file to get meta
    trainer = Trainer(default_root_dir=tmp_path, max_epochs=1)
    tuner = Tuner(trainer)

    changed_attributes = [
        "accumulate_grad_batches",
        "callbacks",
        "checkpoint_callback",
        "current_epoch",
        "loggers",
        "global_step",
        "max_steps",
        "fit_loop.max_steps",
        "strategy.setup_optimizers",
        "should_stop",
    ]
    expected = {ca: getattr_recursive(trainer, ca) for ca in changed_attributes}

    with no_warning_call(UserWarning, match="Please add the following callbacks"):
        tuner.lr_find(model, num_training=5)

    actual = {ca: getattr_recursive(trainer, ca) for ca in changed_attributes}
    assert actual == expected
    assert model.trainer == trainer
Check that all trainer parameters are reset correctly after lr_find()
tests/tests_pytorch/tuner/test_lr_finder.py