Is it possible to import custom source files into Kubeflow components?

I know that Kubeflow only modifies the container with the specified libraries to be installed. But I want to use my custom module in the training Component section of the pipeline.

So let me clarify my case; I'm deploying a GCP Vertex AI pipeline which exists of preprocessing and training steps. And there is also custom library that I created using some libraries like scikit. My main issue is that I want to re-use that library objects within my training step which looks like;

    packages_to_install = [
        "pandas",
        "sklearn",
        "mycustomlibrary?"
    ],
)
def train_xgb_model(
    dataset: Input[Dataset],
    model_artifact: Output[Model]
):
    
    from MyCustomLibrary import XGBClassifier
    import pandas as pd
    
    data = pd.read_csv(dataset.path)

    model = XGBClassifier(
        objective="binary:logistic"
    )
    model.fit(
        data.drop(columns=["target"]),
        data.target,
    )

    score = model.score(
        data.drop(columns=["target"]),
        data.target,
    )

    model_artifact.metadata["train_score"] = float(score)
    model_artifact.metadata["framework"] = "XGBoost"
    
    model.save_model(model_artifact.path)```