why upsampling on binary class problems only?

[akanshajainn]

class DeepMojiBatchSampler(object):
    """A Batch sampler that enables larger epochs on small datasets and
        has upsampling functionality.
    # Arguments:
        y_in: Labels of the dataset.
        batch_size: Batch size.
        epoch_size: Number of samples in an epoch.
        *upsample: Whether upsampling should be done. This flag should only be
            set on binary class problems.*
        seed: Random number generator seed.
    # __iter__ output:
        iterator of lists (batches) of indices in the dataset
    """

Read this on your https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983, is there any underlying important logic behind this which can be applied to the concept of upsampling in general?