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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def get_similars_idxs(cls, learn, layer_ls, **kwargs):
        "Gets the indices for the most similar images in `ds_type` dataset"
        hook = hook_output(learn.model[layer_ls[0]][layer_ls[1]][layer_ls[2]])
        dl = learn.data.fix_dl

        ds_actns = cls.get_actns(learn, hook=hook, dl=dl, **kwargs)
        similarities = cls.comb_similarity(ds_actns, ds_actns, **kwargs)
        idxs = cls.sort_idxs(similarities)
        return cls.padded_ds(dl, **kwargs), idxs