def METHOD_NAME(self):
    train_ds = KineticsDataset(path=self.dataset_path, frames_per_clip=4, split="train")
    val_ds = KineticsDataset(path=self.dataset_path, frames_per_clip=4, split="val")
    # Initialize with random parameters
    self.learner.model = None
    self.learner.init_model()
    # Store prior parameters
    m = list(self.learner.model.parameters())[0].clone()
    # Fit model
    self.learner.fit(dataset=train_ds, val_dataset=val_ds, steps=1)
    # Check that parameters changed
    assert not torch.equal(m, list(self.learner.model.parameters())[0])