def METHOD_NAME(train_transforms, val_transforms):
    dataset = IDChangeDetectionDataset()
    matcher = MultiModalPredictor(
        query="Previous Image",
        response="Current Image",
        problem_type="image_similarity",
        label=dataset.label_columns[0] if dataset.label_columns else None,
        match_label=dataset.match_label,
        eval_metric=dataset.metric,
        hyperparameters={
            "model.timm_image.train_transforms": train_transforms,
            "model.timm_image.val_transforms": val_transforms,
        },
        verbosity=4,
    )
    matcher.fit(
        train_data=dataset.train_df,
        tuning_data=dataset.val_df if hasattr(dataset, "val_df") else None,
        time_limit=10,  # seconds
    )
    assert str(train_transforms) == str(matcher._matcher._query_processors["image"][0].train_transforms)
    assert str(train_transforms) == str(matcher._matcher._response_processors["image"][0].train_transforms)
    assert str(val_transforms) == str(matcher._matcher._query_processors["image"][0].val_transforms)
    assert str(val_transforms) == str(matcher._matcher._response_processors["image"][0].val_transforms)
    assert len(matcher._matcher._query_processors["image"][0].train_processor.transforms) == len(train_transforms) + 2
    assert len(matcher._matcher._query_processors["image"][0].val_processor.transforms) == len(val_transforms) + 2