def METHOD_NAME(self) -> AugmentationPipelines:
    """Returns the augmentation pipeline for this model."""
    # dictionary to hold any augmentation pipeline
    augmentation_pipelines = {}
    # loop through all input features and add their augmentation pipeline to the dictionary
    for input_feature in self.config_obj.input_features:
        # if augmentation was specified for this input feature, add AugmentationPipeline to dictionary
        if input_feature.has_augmentation():
            # use input feature proc_column as key because that is what is used in the Batcher
            augmentation_pipelines[input_feature.proc_column] = self.input_features.get(
                input_feature.name
            ).get_augmentation_pipeline()
    return AugmentationPipelines(augmentation_pipelines)