"parent error" when using callbacks in lightning.pytorch Trainer

Question

I am trying to replicate the multimodal transformer tutorial shown here in a colab notebook. However, this is a relatively old script and lightning.pytorch has changed significantly. I've adapted it to the new lightning and it runs when I remove the callbacks argument in the Trainer, but when I add the callbacks it throws the following error:

/usr/local/lib/python3.9/dist-packages/lightning/pytorch/utilities/model_helpers.py in is_overridden(method_name, instance, parent)
     32             parent = pl.Callback
     33         if parent is None:
---> 34             raise ValueError("Expected a parent")
     35 
     36     from lightning_utilities.core.overrides import is_overridden as _is_overridden

ValueError: Expected a parent

Here's the fit method that calls the trainer

def fit(self):
    # print(self)
    self._set_seed(self.hparams.get("random_state", 42))
    # self.trainer = pl.Trainer()
    self.trainer = pl.Trainer(callbacks=self.trainer_params)

and the _get_trainer_params method that builds the callbacks list

def _get_trainer_params(self):
        checkpoint_callback = pl.callbacks.ModelCheckpoint(
            dirpath=self.output_path,
            # filepath=self.output_path,
            monitor=self.hparams.get(
                "checkpoint_monitor", "avg_val_loss"
            ),
            mode=self.hparams.get(
                "checkpoint_monitor_mode", "min"
            ),
            verbose=self.hparams.get("verbose", True)
    )

    early_stop_callback = pl.callbacks.EarlyStopping(
        monitor=self.hparams.get(
            "early_stop_monitor", "avg_val_loss"
        ),
        min_delta=self.hparams.get(
            "early_stop_min_delta", 0.001
        ),
        patience=self.hparams.get(
            "early_stop_patience", 3
        ),
        verbose=self.hparams.get("verbose", True),
    )

    trainer_params = [
        checkpoint_callback,
        early_stop_callback,
        self.output_path,
        self.hparams.get(
            "accumulate_grad_batches", 1
        ),
        self.hparams.get("n_gpu", 1),
        self.hparams.get("max_epochs", 100),
        self.hparams.get(
            "gradient_clip_value", 1
        )
    ]

Again, when I run the trainer without the callbacks, i.e. self.trainer = pl.Trainer() the model runs.